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Spectral matching approaches inhyperspectral image processingS. Shanmugama & P. SrinivasaPerumalaa Department of Geology, Anna University, Chennai, IndiaPublished online: 04 Dec 2014.

To cite this article: S. Shanmugam & P. SrinivasaPerumal (2014) Spectral matching approaches inhyperspectral image processing, International Journal of Remote Sensing, 35:24, 8217-8251, DOI:10.1080/01431161.2014.980922

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REVIEW ARTICLE

Spectral matching approaches in hyperspectral image processing

S. Shanmugam and P. SrinivasaPerumal*

Department of Geology, Anna University, Chennai, India

(Received 30 April 2014; accepted 4 October 2014)

Many spectral matching algorithms, ranging from the traditional clustering techniquesto the recent automated matching models, have evolved. This paper provides a reviewand up-to-date information on the past and current role of the spectral matchingapproaches adopted in hyperspectral satellite image processing. The need for spectralmatching has been deliberated and a list of spectral matching algorithms has beencompared and described. A review of the conventional spectral angle measures and theadvanced automated spectral matching tools indicates that, for better performance oftarget detection, there is a need for combining two or more spectral matching techni-ques. From the studies of several authors, it is inferred that continuous improvement inthe matching techniques over the past few years is due to the need to handle andanalyse hyperspectral image data for various applications. The need to develop a well-built and specialized spectral library to accommodate the resources from enormousspectral data is suggested. This may improve accuracy in mineral and soil mapping,vegetation species identification and health monitoring, and target detection. Thefuture role of cloud computing in accessing globally distributed spectral libraries andperforming spectral matching is highlighted. Rather than inferring that a particularmatching algorithm is the best, this paper points out the requirements of an idealalgorithm. With increasing usage of hyperspectral data for resources mapping, thereview presented in this paper will certainly benefit the large and emerging communityof hyperspectral image users.

1. Introduction

Spectral research is the detailed analysis and interpretation of the spectra of materials.Spectral signatures define the characteristics of objects based on their absorptance,reflectance, and transmittance of electromagnetic radiation. Several spectroscopic techni-ques are available to interpret such spectral signatures. With the advent of hyperspectraldata, the dimension of research on spectral signatures and spectral matching is undergoingimmense improvization. Govender, Chetty, and Bulcock (2007), in their review onhyperspectral remote sensing for vegetation and water studies, stated that although multi-spectral imagery is useful to discriminate land surface features and landscape patterns,detailed signatures from hyperspectral imagery allow for the identification and character-ization of materials. The large amount of spectral data produced by hyperspectral imagingnecessitates the development of automated techniques that convert imagery directly tothematic maps (Vishnu, Nidamanuri, and Bremananth 2013). Hyperspectral image datasuch as the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS), Compact AirborneSpectrographic Imagers (CASI) I and II, EO-1 Hyperion, Hyperspectral Digital ImageryCollection Experiment (HYDICE), and Moderate Resolution Imaging Spectroradiometer

*Corresponding author. Email: [email protected]

International Journal of Remote Sensing, 2014Vol. 35, No. 24, 8217–8251, http://dx.doi.org/10.1080/01431161.2014.980922

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(MODIS) are constantly used in spectral matching studies. Besides, specialized hyper-spectral data sets for ocean studies (Portable Hyperspectral Imaging Low-LightSpectrometer (PHILLS), Hyperspectral Imager for the Coastal Ocean (HICO)) and forplanetary studies (OMEGA/Mars Express, Moon Mineralogy Mapper (M3)) are now inuse. With all these advanced developments, there is a need to review the aspects ofspectral matching, which is realized as a potential tool for effective utilization of thesehyperspectral resources. Hence, this paper aims to provide an extensive review on spectralmatching, algorithms and analyses the components and factors influencing their matchingperformance.

A pioneering work on spectral matching is that by Fabian (1967), who developed animaging system for agricultural surveying purposes. This work involved the developmentof x-y light-pen spectrum-matching schemes and subsequent extraction of the signaturesof vegetation types of interest. This work also discussed the confusion that occurs whileidentifying crops of similar spectral characteristics, the occurrence of false alarms, and theneed to reduce matching errors due to illumination.

The availability of abundant spectral data from laboratory and field-based spectro-radiometry and hyperspectral imagery has led to the development of diverse spectraldatabases that are utilized in varied applications. This repository of spectral signatures,called ‘spectral libraries’, further facilitates the spectral matching process. Apart fromstandard spectral libraries such as that of the United Stated Geological Survey (USGS),many research-oriented libraries such as those of the Jet Propulsion Laboratory (JPL),Johns Hopkins University (JHU), and the United Nations Educational Scientific andCultural Organization (UNESCO) have been created.

The need to speed up the search-and-match process using large spectral libraries hasresulted in the automation of matching techniques. Automation algorithms perform wellbecause they look for specific, spectrally defined targets (Manolakis et al. 2009). In arecent work, Parshakov (2012) stated that spectral matching techniques are well suited forautomation due to their ability to map data from different sensors coupled with thereduced need for additional data about the study area.

Spectral matching approaches are classified as (i) similarity match and (ii) identitymatch. In identity match, a match for the unknown spectra is assumed to be present in thespectral library, while in similarity match, the unknown spectra are not available in thespectral library for matching. The matching approaches are further classified as (i)deterministic and (ii) stochastic (Vishnu, Nidamanuri, and Bremananth 2013). In thedeterministic type, algorithms are based on the geometrical and physical aspects of theunknown and reference spectra. These include the Euclidean Distance Measure (ED),Spectral Angle Mapper (SAM), Spectral Correlation Measure (SCM), Binary Encoding(BE), and Spectral Feature Fitting (SFF) techniques. Stochastic algorithms based on thedistributions of the spectral reflectance of target pixels include Spectral InformationDivergence (SID) and Constrained Energy Minimization (CEM).

To accommodate the influencing factors (spectral library, absorption features, a prioriknowledge, false hits) and improve performance, a phenomenal evolution of spectralmatching approaches has occurred, resulting in variants of the basic algorithms. Forexample, in the traditional, deterministic technique – Spectral Angle Mapper (Kruseet al. 1993) – similarity is assessed based on the angle between the reference and targetspectra. By including the hyper-angle measure, a variant of SAM called Modified SpectralAngle Mapper (MSAM) (Staenz et al. 1999) resulted in improved accuracy by decreasingthe illumination effects. Similarly, Spectral Correlation Angle (SCA) and SpectralGradient Angle (SGA) approaches, proposed by Robila and Gershman (2005), utilize

8218 S. Shanmugam and P. SrinivasaPerumal

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the correlation coefficient and gradients in terms of the angle. The correlation coefficient,being a better descriptor of the spectra, led to better performance of SCA in comparisonwith SAM. Implementing the SAM measure for an optimal spectral library resulted in theOptimal Spectral Angle Mapper (Luc et al. 2005). In the Extended Spectral Angle Mapper(ESAM) proposed by Li et al. (2014), false alarms are detected using the red edgeposition. SAM also proved to be a good contributor when used in combination withother approaches. When combined with spectral information divergence measures such asSAM-SID (Du et al. 2004), with amplitude difference measure as Normalized SpectralSimilarity Score (NS3) (Nidamanuri and Zbell 2011a) and Jeffries–Matusita distance asJM-SAM (Padma and Sanjeevi 2014), the SAM approach yielded better accuracy inmatching targets. A detailed listing of several matching approaches and a discussion ontheir advantages and limitations is presented in Table 1.

2. Applications of spectral matching

The advent of spectral matching approaches has widened the scope of hyperspectralremote sensing. Major domains such as image classification, band selection, and targetdetection have benefitted from these approaches. Matching techniques are used to matchclass spectra with the ideal spectra for several applications such as identification of croptype and plant species, and mineral exploration (Thenkabail et al. 2007). These techni-ques have increased the precision and accuracy of hyperspectral image classificationcompared with the multispectral classifiers which are affected by the Hughes phenom-enon of hyperspectral data (Xie et al. 2011). Spectral matching can also assist in theselection of pure pixels (end-members) in a scene for a given target material and it alsosearches the spectral libraries for specific targets (Schwarz and Staenz 2001).Furthermore, end-member extraction and material abundance estimation, which areconsidered as the challenges of hyperspectral image processing, can be automatedusing spectral matching techniques. Gupta and Rajan (2010) used matching algorithmssuch as Euclidean distance, Dynamic Time Warping, Derivative Dynamic TimeWarping, and Constrained Dynamic Time Warping to study the temporal shifts invegetation and crops from pixel to pixel, based on geographical location. Apart fromthe above, spectral matching aids in oil spill detection (Andreoli et al. 2007), discrimi-nating salt-affected soils (Farifteh, Van Der Meer, and Carranza 2007), bathymetrymodelling for ocean studies (Ma et al. 2014), and lithological mapping (Zhang and Li2014). Furthermore, spectral matching in planetary remote sensing was demonstrated byEvans (2007), who used the spectral correlation algorithm to map lunar geologicalfeatures. In another study by Zhu et al. (2006), Spectral Angle Mapper and SpectralFeature Fitting algorithms were used to identify lithological units on the surface of Marsusing OMEGA/Mars Express data. Cahill et al. (2010) implemented Spectral FeatureFitting and Spectral Correlation-based matching and analysed the highland and maresoils of the lunar surface using M3 imagery acquired by Chandrayaan-1. Thus, it will beseen that the number of applications and the quality of feature detection approachesusing hyperspectral images have increased with the advent of several spectral matchingalgorithms.

3. Factors influencing the performance of spectral matching algorithms

Analysis of several studies on spectral matching approaches has provided an insight intothe factors that influence their performance. These approaches have evolved from simple

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Table

1.Salient

features

ofthespectral

matchingapproaches

used

inhy

perspectralremotesensing.

Algorith

mApp

roach

Adv

antages

Lim

itatio

nsKey

references

Distancemeasures

Euclid

eanDistance

(ED)

Com

putesdistance

betweentwo

spectra

Com

putatio

nally

simple.

Sensitiv

eto

differencesin

DN.

Tem

poralshiftsin

spectraare

notcaptured

Com

plex

whenDN

andnu

mber

ofbandsarelarge

Gow

er(198

5)

Dot

Produ

ct(D

P)

Cosineof

anglebetween

unkn

ownandlib

rary

spectrais

compu

ted

Treatsintensity

differencesin

acontinuo

usmanner.Assigns

maxim

umvaluewhenspectra

areidentical

Unsuitableforlargelib

raries.

Spu

riou

speaksov

erlapuseful

peaks

Stein

andScott(199

4)

Least-Squ

ares

Minim

ization(LSM)

Com

putesleast-squaresdistance

Accuratewhenused

with

look

-up

table

Loo

k-up

tablemustcontain

accurately

calib

ratedspectra

Mob

leyet

al.(200

5)

Normalized

Com

pression

Distance(N

CD)

Distancebetweenspectrais

measuredby

astandard

compressor

Resistant

tono

ise.

Requiresless

inform

ationon

data.

Com

patib

lewith

diversedata.

Resolvessimilarity

between

unclearpo

rtions

ofspectra

Depends

oninternal

parameters

ofthecompressor

Cerra

etal.(2011)

Adaptiveversionof

CICR

(dCICR)

Com

binescontinuu

mremov

edandcontinuu

mintact

spectra

Suitableto

characterize

minor,

major,andcombined

absorptio

nfeatures

Spectralvectorsto

bechosen

carefully

toavoidspurious

features

Bue,Merenyi,andCsatho

(201

0)

Z-Score

Distance(ZSD)Differencebetweenreference

spectrum

andclassmean

comparedto

standard

deviation

Perform

swell.Accurate

discriminationof

similar

vegetatio

n.Autom

ated

labelling

capability

Atm

osph

eric

correctio

ninfluences

accuracy.Suitability

forno

n-vegetatio

ntargetsno

tassessed

Parshakov

(201

2)

Mahalanob

isDistance

(MD)

Com

putesstatistical

distance

betweenareferencespectral

vector

andmultiv

ariate

distribu

tionof

points

Works

wellforno

isyspectra.

Employ

edforaccurateretrieval

ofinherent

optical

prop

erties

(IOPs)

Com

putatio

nally

complex

dueto

theinclusionof

additio

nal

covariance

matrix

Gillis,Bow

les,andMoses

(201

3)

(Con

tinued)


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Table

1.(Con

tinued).

Algorith

mApp

roach

Adv

antages

Lim

itatio

nsKey

references

Ang

lemeasures

SpectralAng

leMapper

(SAM)

Ang

lebetweentwospectra

quantifiessimilarity/m

ismatch.Invariantto

scaleand

illum

ination.

Com

putatio

nally

simpleandfast.Availablein

manyim

age-processing

packages

Unsuitableforsimilarmaterials.

Intra-classvariability

invegetatio

nno

taccoun

tedfor

(Bertelset

al.20

05).Intolerant

todiversespectra(Jiao,

Zho

ng,

andZhang

2012

)

Kruse

etal.(199

3)

Mod

ifiedSpectral

Ang

leMapper

(MSAM)

Measuresbo

ththespectral

and

hyper-anglebetweentwo

spectra

Minim

izes

lineareffectsdu

eto

thegeom

etry

ofim

age.

Suitablefordata

with

spectral

variability

Com

plex

tocompu

te.Som

etim

esresults

ininfinity

value

(Thenk

abailet

al.20

07)

Staenzet

al.(199

9)

SpectralCorrelatio

nAng

le(SCA)

Correlatio

nbetweenreference

andtarget

spectrameasuredin

term

sof

angle

Discrim

inates

positiv

eand

negativ

ecorrelationbetween

spectra(Carvalhoet

al.20

00).

Insensitive

togain

andoffset

Onlylin

earrelatio

nbetween

spectraisused.Overstated

values

may

lead

tofalsealarms

Rob

ilaandGershman

(200

5)

SpectralG

radientA

ngle

(SGA)

Measuresspectral

anglebetween

gradientsof

referenceand

target

spectra

Invariantto

illum

ination

Lesseffectiveforanalysingroug

hspectra

Rob

ilaandGershman

(200

5)

Optim

ized

Spectral

Ang

leMapper

(OSAM)

Spectralangleforreference

spectrum

inop

timal

library

ismeasuredagainsttarget

inthe

library

OutperformsSAM

measure

invegetatio

nmapping

.Library

comprises

allreferencespectra

toavoidmismatch

Highlydepend

ento

ntraining

data

Bertelset

al.(200

5)

ExtendedSpectral

Ang

leMapper

(ESAM)

SAM

remov

esno

ise.

SVM

and

VertexCom

ponent

Analysis

used

toextractpu

respectral

library.REPmitigatesfalse

alarms

Ang

le-based

thresholdandREP

resultin

betterdetection

comparedwith

othermetho

ds

App

licability

tomulti-class

mapping

isyetto

beassessed.

May

notbe

effectivewhentwo

ormorepeak

features

present

at72

0nm

Liet

al.(201

4)

(Con

tinued)


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Table

1.(Con

tinued).

Algorith

mApp

roach

Adv

antages

Lim

itatio

nsKey

references

Correlatio

nmeasures

Cross-Correlogram

SpectralMatcher

(CCSM)

Cross-correlatio

nat

different

match

positio

nsin

testand

referencespectraare

calculated

Com

paresmaterialsof

different

albedo

s.‘U

niform

’no

ise

compo

nent

isno

tpresentin

the

results

Subtle

spectral

differencesnot

considered.Abilityto

resolve

issueof

mixed

pixelsisno

tassessed

Van

Der

MeerandBakker

(199

7)

CCSM

Con

tinuu

m-

Rem

oved

Algorith

m(CR-CCSM)

Cross-correlatio

nisassessed

betweencontinuu

mremov

edandreferencespectra

Sub

tlespectral

differences

identified.

Preserves

theshapes

ofcross-correlog

rams

Non-diagnostic

noisefeatures

enhanced

with

absorptio

nfeatures

Van

Der

Meer(200

0)

d CICRCCSM

Com

binescontinuu

m-rem

oved

(CR)andcontinuu

m-intact

(CI)characteristics

Perform

sbetteras

itconsiders

absorptio

nfeatureand

continuu

m-rem

oved

spectra

Apriorikn

owledg

erequ

ired

tolabelmaterials

Bue,Merenyi,andCsatho

(200

9)

Encod

ingmeasures

BitMap

Index-Based

Matching

XORlogicalop

erationused

toassess

similarity

between

know

nandun

know

nderivativ

espectra

Fastandfeasible

processing

Mixed

pixelisno

tconsidered.

App

ropriate

binsize

andbits

representatio

nof

spectral

derivativ

erequ

ired

for

matching

Kim

(2011)

New

BinaryEncod

ing

Algorith

mIm

prov

ised

BinaryEncod

ing

(Scott19

88).Spectraland

spatialdescriptorsof

region

arebinary

codedandmatched

with

target

code

using

Ham

mingdistance

measure

Spatialdescriptorsprevent

mismatch

incase

ofsimilar

spectra.

Improv

edstorageof

hyperspectralinform

ation.

Apriorikn

owledg

erequ

ired

toextractshapedescriptors.Loss

ofradiom

etricinform

ation

during

encoding

ofspectra

Xie

etal.(2011)

Feature-based

matchingmeasures

SpectralFeature

Fitting

(SFF)

Absorptionfeature-based

techniqu

ethat

uses

least-

squarestechniqu

e

Sensitiv

ityto

unique

absorptio

nfeatures

allowsaccurate

identification.

Minim

izes

effectsof

grainsize

variations

andillum

ination(W

arner,

Nellis,andFoo

dy20

09).

Availablein

expertsystem

s

Entirespectrum

isno

tutilizedfor

matching.

Noise

andnatural

variations

resultin

features

similarto

absorptio

n

Clark,Gallagh

er,and

Swayze

(199

0)

(Con

tinued)


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Table

1.(Con

tinued).

Algorith

mApp

roach

Adv

antages

Lim

itatio

nsKey

references

Multi-Range

Spectral

Feature

Fitting

(MRSFF)

Com

putesleast-squaresgo

odness

offitof

multip

leabsorptio

nfeatures

forbestmatching

Entirespectrum

isutilizedand

multip

lefeatures

are

characterizedin

different

wavelengths

Mismatch

dueto

spectraof

certainmaterialsbeingsimilar

inaspecific

wavelengthrang

ebu

tdifferentin

another

Clark

etal.(200

3)

SpectralCurve

Fitting

(SCF)

Atwo-step

automated

procedure

invo

lvingderivativ

epeak

find

inganditerativ

eleast-

squaresfitting

Utilizes

prom

inentabsorptio

npeaksof

derivativ

espectra.

Preserves

shapeof

thespectra

andissuitedto

automated

mineral

expertsystem

s

Extra

peaksdu

eto

interpolation

overlapreal

peaks.Lacks

continuu

mmod

ellin

gas

only

absorptio

nfeatures

are

analysed.Lim

itedto

SWIR

region

Brown(200

6)

Com

binedVariable-

Interval

Spectral

Average

(VISA)

metho

dandSpectral

Curve

Matching

(SCM)

Spectralfeatures

detected

from

thetarget

spectraby

VISA

methodarematched

with

alib

rary

spectrum

usingSCM

Highprecisionin

identifying

mixed

pixelsas

itutilizes

multi-scalesalient

features

and

shapecharacteristicsof

the

pixel

Onlysign

ificantvariations

inthe

spectrum

aredetected

for

fitting

.There

isapo

tentialfor

inclusionof

variations

dueto

noise

Kum

aret

al.(201

4)

Inform

ationmeasures

Con

strained

Energy

Minim

ization(CEM)

Respo

nseof

target

spectrais

maxim

ized

bysuppressing

backgrou

ndspectra

Detectsonetarget

source

ata

time.Perform

sbetterthan

SSV

andMSAM

(Hom

ayouni

and

Rou

x20

04).

Sensitiv

eto

noise.Resultsin

false

alarms.Detectio

nof

small

targetsdifficult.Lesssensitive

toshapeof

spectra(Frolovand

Smith

1999).Lessadaptiv

eto

imagecomplexity

Harsany

i(199

3)

(Con

tinued)


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Table

1.(Con

tinued).

Algorith

mApp

roach

Adv

antages

Lim

itatio

nsKey

references

AdaptiveCosine

Estim

ator

(ACE)

Derived

from

theGeneralized

Likelihoo

dRatio.Uses

covariance

matrixto

identify

thebackgrou

ndandcompu

tetheratio

Invariantto

scalingof

spectra.

Con

stantfalsealarm

onscaling

(ENVIClassic

Tutorial20

14)

Rob

ustfortarget

mismatch,

variability

andbackgrou

ndinterference.Fits

reflectance

andradiance

domains

(Manolakiset

al.20

09)

Prior

know

ledg

eof

training

data

requ

ired

forcompu

ting

covariance

matrix.

Detectio

nis

poor

inthepresence

ofno

ise

(Guo

andOsher

2011)

Kraut

andScharf(199

9)

Locally

AdaptiveCEM

(LCEM)

Mod

ifiedCEM.Sam

ple

correlationmatrixisdesign

edfortarget

spectrum

and

standard

CEM

operator

isapplied

Improv

eddetectionof

small

targetsin

complex

backgrou

nddu

eto

spectral

variability

factor.Moreadaptiv

eto

image

content

Lossof

inform

ationdu

ring

matrix

compu

tatio

n.Detectio

nis

difficultwhentarget

isdo

minantin

prop

ortio

n

FrolovandSmith

(199

9)

SpectralInform

ation

Divergence(SID

)Derived

from

divergence

inform

ationtheory.Measures

prob

ability

ofspectral

discrepancy

Betterqu

antificationof

similarity

than

spectral

angle

(Nidam

anuriandZbell20

10).

Highdimension

alim

ages

accommod

ated

with

outdata

redu

ndancy

approach

(Vishn

u,Nidam

anuri,andBremananth

2013)

Works

mainlyformixed-pixel

target

spectra.

Sho

wsa

confused

dend

ogram

when

used

foracompression

process

(Cerra

etal.20

11)

Chang

(200

0)

Com

binedmeasures

SpectralSim

ilarity

Value

(SSV)

Com

binesSpectralCorrelatio

nSim

ilarity

(SCS)andED

Shape

anddistance

used

toassess

similarity.Unambigu

ousED

resolved

byshapemeasure.

OutperformsSCS,ED,and

MSAM

(Thenk

abailet

al.

2007).

Statistical

inform

ationon

spectral

band

smay

notbe

utilized

GranahanandSweet(200

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Table

1.(Con

tinued).

Algorith

mApp

roach

Adv

antages

Lim

itatio

nsKey

references

HiddenMarko

vMod

el-

Based

Divergence

(HMMID

)

Statistical

measure

ofinform

ationon

distance

betweentwoHMM-m

odelled

spectra

Higherdiscriminability

than

SAM,ED,andSID

dueto

characterizatio

nof

unob

served

spectral

prop

ertiesof

apixel

Com

putatio

nisdifficultfor

complicated

spectrum

.Results

inincreasedhidd

enstates

DuandChang

(200

1)

SpectralInform

ation

Divergence-Spectral

Ang

leMapper

(SAM-SID

)

Com

binesdeterm

inistic

SAM

andstochastic

SID

approaches

inTanandSin

Spectraldiscriminability

is5and

2tim

eshigh

erthan

individu

alSAM

andSID

,respectiv

ely.

Utilizes

band

-wisespectral

inform

ation

Quantitativ

eanglemeasure

isinsufficient

tocomplem

entthe

qualitativ

einform

ationfrom

SID

Duet

al.(200

4)

SpectralInform

ation

Divergence-Spectral

Correlatio

nAng

le(SID

-SCA)

Hyb

ridof

SID

andSCA

measuresTan

andSine

versions.

SCA

results

inhigh

erdiscriminatorypo

wer

ofSID

-SCA

than

SID

-SAM

hybrid

measure

Sensitiv

ityof

SCA

tocertain

wavelengths

affectsresults.

The

sine

versionisno

tconsidered

dueto

lower

similarity

values

NareshKum

aret

al.(2011)

Normalized

Spectral

Sim

ilarity

Score

(NS3)

Com

binesspectral

angleand

amplitu

dedifference

between

referenceandtarget

spectra

Spectralvariability

iscaptured

with

high

accuracy

byinclud

ingam

plitu

deparameter

False-positives/negativ

esoccur.

Extensive

spectral

library

with

standard

data

grou

ping

requ

ired

foraccuracy

Nidam

anuriandZbell

(2011a)

JM-SAM-based

mixed

measure

Com

binesthestochastic

Jeffries–

Matusita

distance

and

determ

inistic

SAM

Higherdiscriminability

than

JMandSAM

Suitabilityto

discriminateand

assess

prop

ortio

nswith

ina

mixed

pixelisyetto

beassessed

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cluster analysis to the advanced technique of automated matching by adapting to thefollowing factors.

3.1. Spectral library

Spectral libraries used for matching are varied by nature. In some approaches, a standardspectral library is used while in others a specialized library is developed. To exploit theefficiency of hyperspectral sensors, a well-populated spectral library is required. Whilethere is a strong need to develop the concept of ‘exemplar spectra’ to enhance spectralmatching capability (Gomez 2001), there are valid reasons for adopting a small spectrallibrary. These would provide a reduced search space and the computational time duringthe process of matching the query spectrum. Studies on the development of a qualityfactor (Q) for spectral matching techniques state that a larger library results in lowerQ-values. This is due to the likelihood of extracting different material types from theunknown in the library (Nidamanuri and Zbell 2011b). It is pertinent to mention here thatintegration of spectral libraries has resulted in increased accuracy of target matching andidentification in several applications. Spectral library transfer in spatial (library spectracollected from many geographical locations) and temporal (library spectra collected atdifferent instances) domains has been attempted for assessing spectral variability amongcrops such as alfalfa, triticale, winter barley, winter rape, winter rye, and winter wheat(Nidamanuri and Zbell 2011a).

Spectral libraries in different applications are referred to as either ‘spectral signaturedatabase’ (Ruby and Fischer 2002), ‘information service’ (Leenaars 2013) or ‘look-uptable’ (Mobley et al. 2005). An ideal spectral library in synchronization with the hyper-spectral sensor is of great significance in matching techniques. Image-processing softwarehas inbuilt spectral libraries developed by USGS, JHU, JPL, and NASA – the AdvancedSpaceborne Thermal Emission and Reflection Radiometer (ASTER). Besides, variousresearch works have been carried out to build extensive and specialized soil spectrallibraries (the Global Soil Spectral Library) (Viscarra Rossel 2008); the Czech soil spectrallibrary (Brodsky et al. 2011); urban features (Santa Barbara library) (Herold et al. 2004);Universiti Putra Malaysia’s spectral library (Nasarudin and Shafri 2011); vegetationspecies (Vegetation Spectral Library 2014) developed by Systems Ecology Laboratoryat the University of Texas with National Science Foundation support; JPL’s HyspIRIEcosystem Spectral Library (Hook 2014); wetlands of indigenous environments (web-based Poyang Lake library (Fang et al. 2007); the coastal wetlands of California, Texas,and Mississippi (Zomer, Trabucco, and Ustin 2009); and the benthic habitats library(Louchard et al. 2003).

In regard to planetary research, spectral data of analogous minerals and collectionsfrom planetary missions have created libraries, with the Reflectance ExperimentLaboratory (RELAB 2014), the Planetary Data System (PDS Geosciences SpectralLibrary 2014), Arizona State University’s Thermal Emission Laboratory (Christensenet al. 2000), and a planetary component of USGS library being typical examples.Furthermore, spectral libraries for analysis of oil spills, phenological crop changes, andmany more applications are also available. With several studies carried out on spectraldatabases, the need to publish and share their components led to the development ofonline spectral libraries, where an interactive user interface is available to view spectralplots. Functionalities for querying and downloading are also available. SPECCHIO(Bojinski et al. 2003) is one such example, having a comprehensive collection fromvaried spectral library projects.


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3.1.1. Construction of spectral libraries

Construction of a spectral library involves the extraction and preprocessing of spectralsignatures, the creation of metadata files for each signature, and storage of the collectedsignatures. The process of collecting and processing spectra varies depending on theirsource (spectroradiometry, hyperspectral images, synthetic measurements, compiledlibraries, and open-source spectrometry). Spectroradiometry comprises laboratory- orfield-based measurements of samples of interest. Preparation of samples and removal ofinstrument noise result in accurate spectral plots. Clark et al. (2007) used four types ofspectroradiometer to measure samples of varying grain size under laboratory conditionsfor USGS. Herold et al. (2004) used an Analytical Spectral Devices (ASD) spectro-radiometer to collect 4500 spectral plots of urban features. Such abundant hyperspectraldata have the potential to contribute to a library within a short duration.

Janja (2012) states that building a spectral library using image spectra avoids theproblem of atmospheric errors, which is dependent on the image and a sensor, and alsorequires an operator to select the appropriate spectra. Shwetank, Jain, and Bhatia (2011)developed a digital spectral library for five rice varieties cultivated in and around Bapaulitown in the state of Haryana in India using EO-1 Hyperion images. In this study, a chainof preprocessing methods such as radiometric correction, geometric correction, selectionof appropriate bands, detection and correction of abnormal pixels, spectral smoothing, andatmospheric correction are performed on the image before the retrieval of reflectancespectra for the library. In another study, a mineralogical spectral library for the Nili Fossaeregion on Mars was constructed using OMEGA/Mars Express data (Daswani 2011). Here,the author suggests that although OMEGA data hold good for angle measurement andstudy of spectral characteristics of large areas, they are affected by noise. The authorfurther states that Compact Reconnaissance Imaging Spectrometer (CRISM) data arebetter suited to correlation-based similarity measures for sites on Mars. Recent examplesof libraries for specific sensors include the efforts of JPL to construct a fully fledgedecosystem spectral library aiming to support users of the Hyperspectral Infrared Imager(HyspIRI). This queryable database, based on the template of the ASTER library, allowsusers to contribute their spectral data related to ecological studies. Similarly, severalresearch proposals convey the potential of building an extensive spectral library forbenthic habitats using the HICO mission.

Some libraries comprise spectral measurements from both spectroradiometry andhyperspectral images. The urban spectral library developed by Herold et al. (2004)includes spectral data measured using the ASD spectroradiometer and from AVIRISdata. Apart from these sources, synthetic or simulated spectra are also used to populatelibraries. A synthetic spectrum is formed by mixing certain proportions of the purecomponents obtained from a spectral library. Louchard et al. (2003) constructed a spectrallibrary for benthic habitats with simulated spectra created using measured values ofbottom reflectance and water-inherent optical properties. Several newly developed stan-dard spectral libraries themselves developed into a source for developing a new spectrallibrary. The ASTER spectral library is a compilation of the USGS, JPL, and JHU libraries(Baldridge et al. 2009). Another example is the Topographic Engineering Center (TEC)library (Ruby and Fischer 2002), which was compiled as a web-based library from variousprojects and their related metadata.

The collected spectral plots are stored within a spectral library using variousapproaches based on their accessibility for users. In regard to the USGS, ASTER, andRELAB libraries, each spectrum file is stored on the website as an ASCII file entry along


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with its metadata. Furthermore, specialized tools in the image-processing and mineralidentification software are available for storing the spectral data as libraries in the nativeformat. Here, the user provides each spectrum file or pixel location in the hyperspectralimage from which the spectra need to be extracted as an input for generation of the library.An advanced form of this concept led to the construction of an online database(SPECCHIO), where spectral data and related ancillary information are stored within adatabase system based on the entity–relationship model. This type of database modelcontributes to open-source spectrometry, where data from varied resources are shared freeof cost and stored at a single location for access.

In addition to online spectral resources, currently portable versions of hand-heldspectroradiometers are designed with user-friendly interfaces where the spectral plotscollected during field visits are stored within the instrument. Another increasingly populartrend is the use of portable and cost-effective DIY spectrometer kits integrated withSpectral Workbench (2014) software, which allows fast and easy collection of spectra atany location (Public Lab Store, 2014). An overview of the characteristics of variousspectral libraries available online, along with a detailed listing on their construction,components, metadata, and accessibility, are presented in Tables 2 and 3.

3.1.2. Technical requirements for construction of libraries

One of the fundamental requirements for constructing a spectral library is the availabilityof appropriate tools for accurate processing of the collected spectra. The need for betterstorage, querying, and accessibility arises while creating an extensive web-based library.In the case of online or open-source spectral libraries, users are allowed to upload andshare their projects. Gomez (2001) discussed the process of obtaining spectral data fromvarious sources in a standard format as one of the characteristics of an ideal library. InSPECCHIO (Bojinski et al. 2003), the user needs to complete an inbuilt form and uploadthe spectral collections in either ENVI or ASCII format. This inbuilt form is a template forcollecting ancillary information on sensor name, type, date of collection, etc.

A database system capable of storing extensive data with integrity is required for allthese transactions. Library searching or querying led to the use of advanced databasemanagement systems such as Postgre SQL. The spectral library constructed at thePlanetary Emissivity Laboratory (PEL) of the German Aerospace Center employs thePostgre SQL database for storing and analysing 3 million spectra collected through theMESSENGER mission. It takes about 700 milliseconds for a simple query on this high-volume database and any range of keywords can be used for accessing relevant spectra.Furthermore, a data pipeline is set in this mission to transfer the collected data to PEL inthe planetary data system format (DLR – German Aerospace Centre 2014).

Such a database is integrated with the Common Gateway Interface (CGI) to enableonline access for users (Bojinski et al. 2003). Besides command-based interfaces, user-interactive tools for visualization are designed to assist in searching the library. Somelibraries can be made available in stand-alone and web-based forms. In the stand-aloneversion, plug-ins related to MATLAB and Post-GIS are designed to directly connect andvisualize the library in the corresponding software. In this context, it is also pertinent tonote that the library architecture plays a major role in the accuracy of the matchingprocess. Commenting on the limitation of sequential library architecture while matchingthe closely similar crop spectra, Nidamanuri and Zbell (2011a) state that the numericalsignificance given to each candidate spectrum in the library through sequential architec-ture will avoid the prospect of identifying subtle differences among the crop spectra.


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Table

2.Overview

ofthesalient

features

ofspectral

libraries

used

inremotesensing.

Spectral

libraryz

Sourceof

thespectra

Nature

ofsamples

Num

berof

spectra

(approximate)

Database

type

User

interface

forview

ing

spectra

Metadata

availability

Convoluted

data

Querying

facility

Special

software

for

analysis

Integration

with

other

software

Authorizatio

nrequired

Dow

nloading

facility

Upgrading

facility

Uploading

optio

n

Spectro-

radiom

etry

Airborne

sensors

Spaceborne

sensors

Com

pilatio

nField

Laboratory

USGS

–p

––

–Earth/planetary

materials

~1300

Web-based

archive

pp

p–

pp

–p

p–

San

ta Barbara

p–

–p

–Urban

materials

~4500

Web-based

archive

pp

pp

–p

–p

p–

ASTER

––

––

pEarth/planetary

materials

~2300

Web-based

archive

pp

–p

–p

–p

p–

SPECCHIO

pp

pp

pMaterialsof

variousprojects

>10,000

MyS

QL

pp

–p

pp

pp

pp

RELAB

–p

––

–Planetary

materials

*Web-based

archive

pp

––

–p

–p

p–

PDS

––

–p

pPlanetary

materials

~2463

Web-based

archive

pp

–p

–p

pp

pp

ASU

–p

––

pPlanetary

materials

>2000

Web-based

archive

pp

–p

–p

pp

pp

VSL

–p

––

pVegetationand

related

landcover

*Postgre

SQL

pp

–p

–p

–p

p

Notes:Web-based

archive–spectrum

andrelatedmetadataarearchived

inweb

interfaceas

ASCIIfiles.

*Detailsof

approxim

atenumbers

notknow

n.


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Table

3.Salient

features

ofthespectral

libraries

used

inremotesensing.

Spectrallib

rary

Review

ofconstructio

nCom

ponentsof

thelib

rary

Metadataavailability

Accessibility

USGSDigital

Spectral

Library

Spectra

measuredusing:

Beckm

an52

70,ASD,Nicolet

Fou

rier

Transform

Infrared,and

NASA’sAVIRIS

radiom

eters.

Mineral

analysisdo

neby

XRD,

electron

microprob

e(EM),

XRF,

andpetrog

raph

y.For

vegetatio

n,do

cumentatio

nrelatesto

locatio

nandspecies

type.Ittook

20yearsto

construct(Clark

etal.20

07)

Current

versionissplib

06awith

1300

spectraof

minerals,rocks,

soils

andmixtures,coatings,

liquids,liq

uidmixtures,

volatiles,frozen

volatiles,man-

madechem

icals,plants,

vegetatio

nmixturesandrelated

microorganism

.andthemoon

andotherplanets.Nextversion

(splib07

)isun

derway

Docum

entatio

nisabou

torigin,

samplepu

rity,andrelated

details.Digitalph

otog

raph

sof

certainsamples

available.

SPECtrum

ProcessingRou

tines

(Specpr)(Clark

etal.19

93)

conv

olvesUSGSlib

rary

spectra

Spectralplotscanbe

downloaded

asASCIIfiles.Older

versions

(splib05

andsplib

04)arealso

prov

ided

with

conv

olved

libraries

that

areem

bedd

edin

severalim

age-processing

packages

forspectral

analysis

Santa

Barbara

Urban

Spectral

Library

Fou

rcatego

ries

ofspectraare

listed.

Eachspectrum

ismeasured(at35

0–24

00nm

)for

discriminatoryability

usingthe

Bhattacharya

distance

(Herold

etal.20

04).

4500

spectrarepresentin

g10

8unique

urbantargets(roofs/

build

ings,transportatio

nsurfaces,andno

n-bu

ilt-up

surfaces).Spectra

conv

olvedto

AVIRIS

wavelengthrang

es

Digitalph

otog

raph

sforsome

urbantargets.Location

coordinates,classnames,and

spectranamearepresent

Interactivemap

displaying

Fairview

andCathedral

Oak

sitesandtheirtargetsof

Asphalt

road.Spreadsheet

fortargets

availablefordo

wnloading

.AVIRIS

conv

olvedlib

rary

and

ASD

spectraof

roofs,

transportatio

n,andno

n-bu

iltup

surfaces

canbe

accessed

ASTERSpectral

Library

Com

piledfrom

USGS,JH

U,and

JPLlib

raries.Con

tributions

wereconv

ertedto

acommon

standard

andancillary

data

are

includ

ed.Spectra

obtained

byBeckm

an,Perkin,

Perkn

ic,and

Nicolet

radiom

eters(Baldridge

etal.20

09).

The

currentversion(2.0)contains

2300

spectra(at0.4–

15.4

µm)

ofrocks,minerals,snow

,ice,

terrestrialsoil,

meteorites,and

vegetatio

n.JPLlib

raries

are

updatedregu

larly

Eachspectrum

issupp

liedwith

details

oftype,class,particle

size,samplenu

mber,ow

ner,

wavelengthrang

e,origin,and

measurementtype.

Latestversion(2.0)canbe

downloadedor

obtained

asCD-

ROM.The

website

contains

aninteractivesearch

tool

toqu

ery

andview

thespectraof

different

targets

(Con

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Table

3.(Con

tinued).

Spectrallib

rary

Review

ofconstructio

nCom

ponentsof

thelib

rary


Accessibility

SPECCHIO

database

Acomprehensive

database

ofspectrafrom

USGS,JH

U,JPL,

USDA

Beltsville,field

campaign,

limno

logy

and

vegetatio

nstud

ies,and

parametricmod

els.A

data

mod

elisdesign

edforeffective

organizatio

nof

inform

ationin

anentity–

relatio

nship

fram

ework.

Users

can

contribu

tein

either

ENVIor

ASCIIform

atwith

ancillary

data

(Bojinskiet

al.20

03).The

database

works

byMyS

QLand

JAVA

applications.Plug-insfor

Arc

GIS

andMATLAB

availableforqu

erying

,view

ing,

andinpu

tof

spectraand

metadata.

Current

version(V

3.1)

contains

60fieldcampaigndata(111,202

spectraof

varioustargets).

~480

2contribu

tions

arefrom

spectral

libraries.Library

isconstantly

updated.

Eachspectrum

isprov

ided

with

theancillary

details

classified

asgeneral,po

sitio

n,mod

el,sensor,

target

type,andland

-use

type.

Tertiary

inform

ationforeach

ofthesecatego

ries

isavailable

Anauthorized

user

canaccess

and

downloadthedatabase

applicationandinstallin

the

localsystem

.Onlineversion

canalso

beused

tostoreand

retrieve

requ

ired

data

Reflectance

Exp

erim

ent

Laboratory

(RELAB)

Spectra

obtained

from

bidirectionalandFTIR

spectroradiometer.Requestfor

spectral

measurementand

inclusionin

thedatabase

canbe

sent

alon

gwith

thesample.

Ancillarydata

abou

tsample

name,

origin,particle

size,

texture,

locatio

n,target

type,

andsubtyp

eareto

besubm

itted

Databaseof

Version

2006

acomprises

spectrarelatedto

projectsthat

includ

eplanetary

samples

andtheirterrestrial

analog

ues

Stand

arddeviationmeasuresare

prov

ided

alon

gwith

thespectral

plot.Ancillarydata

are

availableforeach

spectrum

file

Spectralplotscanbe

downloaded

incompressedform

atfrom

the

website.Spectralfilesarein

both

ASCIIandtext

form

at.

Users

cansend

theirsamples

toRELABforob

tainingspectral

measurementandsubsequent

uploadinginto

thelib

rary.

(Con

tinued)


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Table

3.(Con

tinued).

Spectrallib

rary

Review

ofconstructio

nCom

ponentsof

thelib

rary


Accessibility

Planetary

Data

System

(PDS)

Geosciences

Spectral

Library

Builtby

incorporatingthe

Com

pact

Recon

naissance

ImagingSpectrometer

(CRISM)

spectral

library

anddata

from

otherprojects.CRISM

library

comprises

2260

spectrafrom

1134

analog

uematerialsfrom

Mars.Users

cancontribu

tespectraby

subm

ittinga

prop

osal

Library

contains

measurements

relatedto

earth,

lunar,and

meteorite

materials.Spectra

of12

28specim

ensand24

63prod

uctsarepresentin

the

currentarchive

Metadataon

prod

uctID

,spectral

rang

e,prov

ider

ID,

measurementgeom

etry,

instrumentused,azim

uth,

incidence,

emission

,andph

ase

angleareprov

ided

Web

interfaceprov

ides

toolsfor

search,display,

and

downloading

.Searchisbased

onkeyw

ords,specim

enlocatio

n,texture,

compo

sitio

n,andsupp

lierinform

ation.

Interactivegraphplot

canbe

view

ed.Spectralplotsare

available.CRISM

library

canbe

downloaded

Arizona

State

University

(ASU)

Therm

alEmission

Laboratory

Spectralmeasurementsof

hand

-processedpu

reanalog

uesof

Martianrocks(710–1

000nm

)at

5–45

µm

areob

tained

using

Mattson

Cyg

nus-10

0interferom

etricspectrom

eter

(Christensen

etal.20

00).Users

canup

load

theirspectral

data

into

thelib

rary

with

metadata

Spectra

arearchived

inthecurrent

version(v

1.1)

ofASU

library

Ancillarydata

onsample

compositio

nandquality,

catego

rizedas

generalinfo,

spectral

info,microprob

eanalysis,bu

lkanalysis,XRD

analysis,andmod

almineralog

yareavailableat

theweb

interface

Users

canaccess,search,

download,

andup

load

the

spectral

data.Searchfilters

are

basedon

library

albu

ms,sample

name,

type,subg

roup

,and

quality.Spectralplotscanbe

view

edandexpo

rted

indifferentfile

form

ats

Vegetation

Spectral

Library

(VSL)

Builtusingspectraof

vegetatio

nandland

covercollected

from

differentresearch

projects.

PostgreSQLdatabase

isused

for

storage,

query,

andretrievalof

data

Alsocomprises

anextensive

collectionof

spectraof

mixed

vegetatio

n

Metadataon

file

name,

locatio

n,land

-cov

ertype,weather,date

andtim

e,instrument,and

sensor

areprov

ided.Digital

photog

raph

sof

samples

are

append

ed

Web

interfaceallowsforsearch

basedon

metadataand

downloading

ofthespectra

files.Users

cancontribu

tetheir

spectracollections

inASCII

form

at


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Listing the limitations of the database models, Rasaiah et al. (2011) mention that, as inthe case of SPECCHIO (Bojinski et al. 2003) and Hyperspectral.info (Ferwerda, Jones,and Du 2006), these models have no mechanisms for tracking updates and transactionswithin the database. Furthermore, there is no protocol for assuring data quality. Theauthors also cite the static nature of the data-storing capacity in the ASTER and USGSspectral libraries to be a limitation, despite their comprehensive structure. These limita-tions, as cited by the authors, were overcome in a data warehousing model which has aunique mechanism for tracking updates, thereby preventing data corruption and flaggingthe metadata with their respective quality measures. Further, the authors confirmed theefficiency of data warehousing models to store spectral libraries and hyperspectral data-bases from several sources in the form of a centralized repository. In another study, theauthors (Rasaiah et al. 2012) state that metadata are a central component in such modelsand suggest certain standardized, in situ metadata collection-and-documentation proce-dures that can facilitate data exchanges.

With the development of extensive libraries, the inclusion of different and currentsensor systems has to be ensured. Spectra derived using hand-held spectroradiometry andhyperspectral images differ in terms of wavelength ranges. To solve the issue of compat-ibility, spectral libraries from the USGS provide convolved spectra for different sensors.

3.2. Spectral features

The spectrum of a material is characterized by the presence of absorption features whichare exploited for accurate matching. Stein and Scott (1994) proposed the probability-basedmatching (PBM) method for a mass spectral database. This technique is based on thepeaks in the target spectrum and in the library spectrum falling within a predefinedabundance window. Here, the non-matching peaks in the target spectrum are treated asimpurities and are ‘flagged’. Another algorithm used by the authors is the Dot Productmeasure, which continuously compares the peak intensity differences in the library andreference spectra and yields maximum value in the case of a bad match. Furthermore, theauthors improved the performance of the Dot Product algorithm by adding weights to therelative intensities for spectra with many common peaks. The limitation of this approachis that, in some instances, absorption peaks are confused with spurious peaks. The flatresponse regions of a spectrum with added noise take on the appearance of small peaks.The application of the de-noising techniques might eliminate the useful absorption peaks.Spectral library search tools such as QuickMod employ spectrum–spectrum matching andscoring algorithms, where a weight is given to the intensities of matching and non-matching peaks (Ahrne et al. 2011). The small informative peaks and overlapped peaksin the spectra cannot be easily identified by the software and hence the shape of the peakcannot be described accurately. To overcome this difficulty, point-to-point matchingalgorithms were developed where all the points in the spectra are used (Li et al. 2006).Lau, Hon, and Bai (2000) developed the Effective Peak Matching technique where thepositions of three peaks in the sample spectrum are compared to the three largest peaks inthe selected reference spectrum. All reference spectra with matching peaks are selected.

The complexity of a spectrum increases with the presence of many peaks of varioussize. Stein and Scott (1994) attributed multiple major and minor peaks to such a complex-ity. The authors realized the limitation of the Dot Product algorithm in handling smallspectral peaks. Continuous improvisations have been made in eliminating spurious peakeffects and identifying real peaks (Levin 1999; Hansen and Smedsgaard 2004; Coombeset al. 2005). Clark, Gallagher, and Swayze (1990) proposed the Spectral Feature Fitting


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(SFF) algorithm, which utilizes the inherent absorption patterns between two spectra formatching. This technique was modified as Multi-Range Spectral Feature Fitting (MRSFF)(Clark et al. 2003), where the absorption features at various wavelength ranges areconsidered for matching. However, similar materials may not always have matchingspectra at different ranges of wavelength. To overcome this issue, Pan, Huang, andWang (2013) devised a variance–covariance weight method for MRSFF. Here, differentialweights are assigned to the wavelength ranges of the spectrum based on their importancefor matching. Kumar et al. (2006) introduced the Variable Interval Spectral Average(VISA) method, where variance is computed for each region in the spectrum. When thevariance exceeds a threshold, the presence of a spike is confirmed. The position and linewidth of these features are stored and used as a reference for matching. In this approach,an appropriate threshold needs to be used to avoid noise features, which can also result inhigh variance like that of the absorption troughs.

Hence it could be said that starting with the spectral feature fitting technique (Clark,Gallagher, and Swayze 1990), several algorithms that utilize the patterns of the peaks andtroughs have evolved.

3.3. Illumination effects

Illumination-independent spectral signatures for matching are obtained after radiometriccorrection. In some cases, mismatch occurs when two spectra of similar materialsoriginate from surfaces of different orientation. Hence, a measure for the likelihood oftheir equality has to be applied through a threshold, for which a precise knowledge of thesurface orientation and a temporal comparison of identical objects illuminated fromdiffering sun angles is required (Wiemker and Hepp 1994). The Spectral Angle Mapper(Kruse et al. 1993) and Spectral Gradient Angle (Robila and Gershman 2005) algorithmsare insensitive to illumination differences in the spectra. Illumination effects can bemitigated by scaling each signature in the library by its Euclidean norm. Such a scalingwill discard the geometric albedo in the signatures, but preserve the spectral angles (Bue,Merenyi, and Csatho 2010). Nidamanuri and Zbell (2011a) collected spectral measure-ments only during the hours 11.00–13.00 to minimize the effects of illumination. Inanother study, Schiefer, Hostert, and Damm (2005) analysed the effects of view angleof hyperspectral data for urban areas, and stated that illumination effects severely influ-ence the spectral information.

3.4. A priori knowledge

In a few cases of spectral matching, a priori knowledge of background spectral informa-tion increases the accuracy of spectral matching. The adaptive coherence estimator (ACE),also known as the Adaptive Subspace Detector, which is an extension of the generalizedlikelihood ratio test (GLRT), employs a covariance matrix to identify the backgroundinformation (Kraut and Scharf 1999). In contrast, the template-matching approach invol-ving l1 minimization requires neither background information nor the assumption ofmultivariate normal distribution of the background during computation. The l1 minimiza-tion algorithm, however, depends only on the spectrum of the material of interest, whichin turn is dependent on the end-member extraction process (Guo and Osher 2011). Otheralgorithms that use more information from the reference spectrum include Mean SquaredError Statistics (MSES) (Staenz, Schwarz, and Cheriyan 1996), which uses per-band


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noise, and the Cross-Correlogram Spectral Matcher (CCSM) (Van Der Meer and Bakker1997), which provides more than a single-measure spectral similarity.

Xie et al. (2011) suggested an improvised version of the binary encoding algorithmthat uses shape descriptors such as area, asymmetry, elliptic fit, rectangular fit, ratio oflength-to-width, and compactness of the spectra for matching. Thus, it could be inferredthat the integration of a priori knowledge during spectral matching can reduce theoccurrence of false hits and enhance algorithm performance.

3.5. False hits

False hits are defined as poor matches of target and library spectra, which are assigned ahigh matching score. The top-scoring library spectrum is labelled as the perfect match. Inspectral searching, the top-score matches that have turned out to be a mismatch are termedfalse-positives. This refers to the number of non-target pixels incorrectly classified as thetarget (Homayouni and Roux 2004). A target spectrum may be matched with the referencespectrum of a different material due to (i) the scoring function assigning a reasonablescore to a poor spectral match and (ii) a low-quality library spectrum. A low-qualitylibrary includes noisy and contaminated query spectra. In a study of point-to-point patternmatching, Li et al. (2006) stated that even a correlation coefficient of r = 0.99 does notmean a match in all situations. It is the analyst’s responsibility to decide whether thespectral match corresponds to the real situation. While comparing the mismatch and real-time spectra, Manolakis et al. (2009) concluded that in practice, the target signature iseither imperfectly measured leading to mismatch or it exhibits spectral variability. Robustmatched filter algorithms use covariance regularization to address this problem of mis-match due to spectral variability. While studying citrus greening disease at the CitrusResearch and Education Center (CREC), Central Florida, USA, using airborne hyper-spectral images, Li et al. (2012) set the red edge position (REP) at 720 nm and filtered thefalse-positive pixels from the result of SAM-based matching. The pixel identified as‘healthy’ by SAM, but with REP below 720 nm, is categorized as false-positive andfiltered. This approach may not work well in some instances because in continuousspectra there may be a double-peak feature near 700 and 725 nm causing a disruptionin relating the REP factor to the health status of the crop. This conclusion is similar to thatof Clevers, Kooistra, and Salas (2004). In such a case, the second derivative spectra willbe required for accurate extraction of the REP factor. Though many causes for mismatchhave been listed above and reviewed, the study of other types of mismatch needs a fullinvestigation.

3.6. Distortion of spectra due to environmental factors

Hyperspectral data, derived either from hand-held spectroradiometer or hyperspectralimages, could be distorted due to several environmental factors, thus resulting in mis-match. An example of such a distortion is the vegetation index profile with differentgrowing practices (sowing, senescence, harvest, etc.) for two similar vegetation classeswhich appear as points separated by large distances. This results in labelling same speciesas different classes. Modification-tolerant tools are being developed where, even if part ofthe target spectrum is found, the result might be a number of partially matched hits (Liet al. 2004; Ahrne et al. 2011). Such instances of spectral matching with environmentallymodified spectra were reported during an oil spill analysis (Salem, El-Ghazawi, and


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Kafatos 2001; Li et al. 2004), and during quality control of herbal medicines (Chau et al.2001).

3.7. Threshold requirement

Thresholding is an important step in spectral matching, used for selecting the ranges in thespectra, identifying false alarms, and flagging peaks. Despite the availability of compe-titive techniques of selecting a threshold, it still remains a difficult task because an optimalthreshold has to maintain a low number of false alarms and a high number of correctdecisions. Hence there is always a compromise in choosing a low threshold to increase theprobability of detection and a high threshold to decrease false alarm rates. (Manolakis,Marden, and Shaw 2003). According to Schwarz and Staenz (2001), the adaptive thresh-old technique, which is based on the training area statistics, can be used in combinationwith spectral matching techniques to classify the spectra. This technique was demon-strated using the Modified Spectral Angle Mapper (MSAM) on simulated data andHyperspectral CASI imagery collected over an agricultural site in southern Manitoba,Canada. In the case of the binary coding technique, simple segmentation of the spectruminto a set of uniformly sized sub-ranges can give 100% separation. The method ofchoosing multiple thresholds in an improvised binary coding consists of determiningthe mean brightness of a pixel vector and setting the upper and lower thresholds (Jiaand Richards 1993). Li et al. (2012) arrived at a threshold value based on a trial-and-errorprocess. In this study on mapping disease-prone citrus, the threshold value that resulted inhigh detection accuracy from one of the three values (0.05, 0.1, and 0.15) was used.

Galal, Hassan, and Imam (2012) introduced the concept of ‘learnable hyperspectralmeasures’ to overcome the limitations of using a static threshold in assessing similarity ordissimilarity between two spectra. In this study, the authors consolidated the statisticsobtained from nine matching measures for creating the combined similarity and dissim-ilarity pattern, which was then classified using the Support Vector Machine (SVM). Theclassifier component provides the required adaptive similarity threshold, resulting inprecise material identification.

Hence, proper setting of the threshold can help immensely in improving the perfor-mance of spectral matching approaches since these control interference by spurious peaks,false alarms, and mixed pixels.

3.8. Mixed pixels

With a diverse range of targets and backgrounds, matching approaches have to beequipped in tackling the mixed pixels in an image. Although a reasonable level ofidentification has been achieved for pure target spectra, the identification level for targetmixtures is yet to be addressed (Vishnu, Nidamanuri, and Bremananth 2013). Severalmatching approaches were insensitive to this aspect. In one study on template matching,the l1 minimization-based approach was developed to work on an inhomogeneous back-ground. Here, the algorithm obtains the correct matching pixels if the pure material’scontribution to the mixed pixel is higher (Guo and Osher 2011). The Hidden MarkovModel Information Divergence (HMMID) method, which is a statistical approach, pro-vides enhanced capability in capturing spectral variability and is effective in identifyingmixed spectra (Du and Chang 2001). Manolakis et al. (2009), in a work on identifying thebest hyperspectral detection algorithm, explained the need for spectral matchingapproaches to detect subpixel targets efficiently. In this work, the target is modelled and


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identified based on statistics such as mean, variance, and covariance, which are calculatedfrom the background information. In the case of the availability of a spectral library, allthe candidates are used to create a low-dimensional subspace or background for identify-ing the subpixel target. However, in this approach, a detailed knowledge of the back-ground and proper selection of threshold is required for accurate matching. The traditionallinear unmixing algorithm, which decomposes the subpixel into its components based onthe Euclidean distance measure, is sensitive to the magnitude of the spectrum. To over-come this issue, Chen et al. (2009) in their study on mapping biological soil crusts inGurbantunggut Desert, China, proposed a subpixel framework integrating spectral match-ing measures such as SAM, SCM, and SID to identify the best match for the unknownspectrum to a weighted sum of end-member spectra. This technique, based on theSequential Quadratic Programming (SQP) method, provided improved identification ofmixed pixels compared with the existing unmixing approaches. However, it should benoted that a well-built spectral library with the required mixture components is necessaryfor such improved performance.

4. Improvised and combined algorithms

The limitations of spectral matching techniques can be overcome by certain modificationsto the existing version. For example, although the SAM averages out the absorptionfeatures required for efficient discrimination, it is unsuitable for discriminating closelyrelated materials. Besides, it cannot distinguish the negative and positive correlationbetween the target and the reference. Staenz et al. (1999) developed the MSAM, whichcombines both the shape and magnitude of spectra. It thus overcomes the limitation ofSAM, which uses only the shape feature. Similar improvisations led to the development ofalgorithms such as the Normalized Euclidean Distance (NED) (Robila and Gershman2005) and Cross-Correlogram Spectral Matcher–Continuum Removed (CCSM-CR) (VanDer Meer 2000). The common factor in both algorithms is that matching performance isincreased by modifying the nature of the input spectrum. While NED deals with normal-ized spectra, the CCSM-CR approach makes evident the role of the continuum removalmethod as a precursor for matching.

The most effective method of improving the performance of spectral matchinginvolves the combination of two or more qualitative measures with the qualitativemeasures of matching. According to Vishnu, Nidamanuri, and Bremananth (2013), acombination of methods results in increased accuracy. Examples of combining twomatching measures include Spectral Similarity Value (Homayouni and Roux 2004;Granahan and Sweet 2001), Spectral Information Divergence–Spectral Angle Mapper(SAM-SID mixed measure) (Du et al. 2004), Spectral Information Divergence–Spectralcorrelation angle (SID-SCA mixed measure) (Naresh Kumar et al. 2011), NormalizedSpectral Similarity Score (NS3) (Nidamanuri and Zbell 2011a), and Jeffries–Matusita-Spectral Angle Mapper (JM-SAM) (Padma and Sanjeevi 2014). Homayouni and Roux(2004) proposed the fusion of Spectral Similarity Value (SSV), Constrained EnergyMinimization (CEM), and MSAM for precise matching. Here the authors confirmed thatalthough primary algorithms are important in identifying pure pixels, the hybrid techni-ques have considerable scope to increase the accuracy of the matching process. Similarly,while identifying land-use classes, Thenkabail et al. (2007) concluded that the SSVtechnique, which captures both the distance and shape measure of the spectra, is betterthan the Euclidean Distance, Modified Spectral Angle Similarity, and Spectral CorrelationSimilarity measures. It should be noted that the combined measures (SID-SAM, SID-SCA,


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and NS3) handled intra-species and inter-species variability more effectively than theindividual algorithms. For instance, Dudeni and Debba (2009) described the improvedmatching ability of SID-SAM in characterizing seven spectrally similar African savannahtree species. Naresh Kumar et al. (2011) reported that the hybrid SID-SCA measureyielded an increased matching accuracy in discriminating five varieties of Vigna species.Similarly, Nidamanuri and Zbell (2011a) indicated the complementary nature of spectralamplitude and shape measures in NS3 for efficient classification of six crop varietiescollected at different phenological stages. Hence, a combination of quantitative andqualitative spectral matching techniques helps to improve accuracy and strengthen resultscompared with those obtained using individual techniques.

5. Performance measurement

The efficiency of spectral matching techniques is assessed by various measures ofperformance. The Relative Spectral Discriminability Probability (RSDPB) (Dudeni andDebba 2009) computes the likelihood that a spectrum will be identified by a selective setof spectral signatures. The higher the RSDPB value, the more likely the spectra will bediscriminated from others in that region of the electromagnetic spectrum.

In some instances, the performance of a new spectral matching approach is comparedto and evaluated against a standard approach such as the Spectral Angle Mapper. Singh,Ramakrishnan, and Mansinha (2012) evaluated matching of the target and modelledspectrum based on root mean squared error and matching scores. Qualitative approachessuch as knowledge in judging the spectral identification also exist (Arora et al. 2013).Bue, Merenyi, and Csatho (2009) devised the concept of Visual Score (VS) for assessingthe discriminatory capacity of matching techniques. The authors inspected the matchingtrends manually and assigned a score for each method. Apart from these, statisticalmethods such as Probability of Spectral Discrimination (PSD) (Chang 2003) and thePower of Spectral Discrimination (PWSD) (Van Der Meer 2006) have been used to assessthe reliability of spectral library searches in geological material mapping. PSD measuresthe probability that a spectral library or its subset will be able to identify an unknownspectrum relative to other spectra in the library. In contrast, PWSD allows the calculationof spectral confusion between the target and reference spectra by using a set of spectralsimilarity estimates. According to Nidamanuri and Zbell (2011b), both PSD and PWSDindicate the quality of a spectral library and the search results. The authors proposed theQ-factor, which functionally performs a similar role by assessing the reliability of spectralidentification. Besides, it quantifies the performance of the spectral library search methodrelative to the nature and type of the unknown materials and composition of the spectrallibrary. Another accuracy assessment method, by Manolakis et al. (2009), which utilizesthe Receiver Operating Characteristic (ROC) curves, plots the probability of detectionversus the probability of false alarm. The ROC estimation depends on the number ofavailable target and background pixels. Since the number of target pixels is often limited,the ROC method should be used in empirical detection evaluation with extreme care.

The lack of widely accessible data from well-designed algorithms with accurateground truth makes the experimental evaluation and comparison of algorithms a difficultprocess. In some cases, the implementation of the matching technique is tested usingsimulated spectra (Guo and Osher 2011). This alone may not be helpful for establishingthe superiority of the technique used. Hence it is necessary to use a good performancemeasure that identifies the influence of the quality of the spectral library, false alarms, andintrusion of background information over the accuracy of matching technique.


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6. Spectral matching tools

Development of interactive interfaces for visualizing spectral analysis has evolved withthe emergence of several specialized spectral libraries and matching approaches. Imagingsystems that emerged in this context during the early days include the Spectral ImageProcessing System (SIPS) (Kruse et al. 1993), Imaging Spectrometer Data AnalysisSystem (ISDAS) developed by the Canadian Centre for Remote Sensing in 1995, andUSGS’s Tricorder (Clark, Gallagher, and Swayze 1990). These systems have the basicfunctionalities for data input/output, interactive visualization, and analysis of imagingspectrometer data. A single algorithm such as that of SAM in SIPS, SFF in Tricorder, orMRSFF in Tetracorder govern spectral matching for applications mostly related to mineralmapping. Subsequently, based on these algorithms, an automated rule-based identificationtool called the Spectral Expert System was integrated with ENVI in 2007 as a plug-in.This expert system also works based on the principle of matching absorption features andhence has modules for continuum removal. This system worked for three kinds of input:single-target spectrum, entire spectral library, and hyperspectral image. All these devel-opments led to automation of several matching techniques and enabled their incorporationinto image processing software and mineral identification tools.

A brief look at the automated spectrum-matching tools characterizes the applicationsof these techniques to various domains, including biotechnology. The analysis of peptides,which are the chains of amino acids linked by chemical bonds, is of great significance inbiotechnology. Though peptide and hyperspectral image spectra vary considerably, thecontinuous development of automated tools of spectrum matching for peptide identifica-tion serves as a reference for hyperspectral image processing systems. Lam et al. (2007),in their work on the development of a spectral library search method for peptideidentification, stated that the SpectraST tool performed better than the SEQUEST tool.SpectraST is based on spectral searching while SEQUEST is based on sequence search-ing. SEQUEST developed by the Yates laboratory operates on the principle of cross-correlation for assessing spectral similarity. In the case of satellite image processingsoftware, ENVI, ERDAS, and PCI Geomatica include spectral analyst tools for hyper-spectral data analysis. A typical example of an automated target detection tool, availableas the Material Identification tool of Tactical Hyperspectral Operations Resource (THOR)workflow in ENVI version 5.1, is shown in Figure 1. The THOR workflow’s userinterface and its matching capability are depicted in Figure 2, where the unknown targetspectrum chosen from the AVIRIS image is matched with an appropriate candidatespectrum from the USGS library. Similarly, the Spectral Analysis Workstation inERDAS Imagine version 8.6 has routines for automated target identification and materialmapping. An overview of the capabilities of the spectral matching modules in thesesoftware programs and other stand-alone tools is presented in Table 4. Furthermore, adetailed note on their structure, operating principle, and function is given in Table 5.

It can thus be inferred that there is an increasing trend in the development of user-friendly interfaces for spectral matching and their subsequent influence on materialmapping. It is, however, important to analyse the integration of various specializedspectral libraries and the convolved multispectral and hyperspectral sensor-derived spectrafor varied environmental and resource-mapping applications. A recent tool is theadvanced mass spectral database in the form of a cloud computing. Cloud computing isa large networked environment of shared software, databases, and other computingresources from a variety of architectures. An example is the ‘mzCloud’ (Mistrik et al.2013), which comprises a web-based interface with a large collection of libraries,


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appropriate metadata, and an automated target identification algorithm. One of the keyfeatures is that even if a target spectrum is not represented as a reference candidate in alibrary, this cloud-based community tends to identify the structural information of suchinput by relating several libraries. Though Rasaiah et al. (2011) opined that the lack of

Figure 1. Illustration of automated spectral identification using the Tactical HyperspectralOperations Resource (THOR) workflows in Exelis Visual Information Solutions 5.1 V (ENVI5.1). The best match for the unknown spectra is assessed by the SAM approach.

0.75

0.80

0.85

0.90

0.95

1.00

1.05

1.10

2.0 2.1 2.2 2.3 2.4 2.5Wavelength (µm)

Ref

lect

ance

(×1

00)

%

Target spectrum X:538 Y:536Library spectrum alunite3.spc

Figure 2. Target and library spectrum of alunite matched using material identification tool.


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Table

4.Overview

ofthesalient

features

ofthespectral

matchingtools.

Software

Stand-alone

software

only

for

matching

Separate

modules

availablefor

matching

Graphical

user

interface

Com

mand-

based

operation

Preprocessing

tools

Contin

uum

removal

Matching

approach

Autom

ated

target

detection

tools

Inbuilt

spectral

libraries

User-

defined

spectral

libraries

Spectral

saving

capability

Creation

of

spectral

library

Querying

capability

Application

SIPS

p–

pp

––

SAM

–p

–p

––

MTetracorder

p–

pp

pp

MRSFF

–p

–p

––

T/P

PCI Geomatica

–p

pp

p–

SAMMSAM

–p

pp

p–

C

ENVI

–p

pp

pp

Multip

le*

pp

pp

p–

Multip

le**

ERDASIM

AGIN

E–

pp

–p

–Multip

le*

pp

pp

p–

Multip

le**

TSG

p–

p–

––

Aux

Match

pp

pp

pp

MSPECMN

p–

p–

––

Feature

Search

pp

pp

pp

M

DARWIN

p–

p–

––

Weighted

Score

pp

pp

pp

M

Notes:M,Mineral

Mapping;T/P,Terrestrial/Planetary

Applications;C,Classification.

Multip

le*,

Com

prises

severalmatchingapproaches

(adetailedlistisshow

nin

Table3).

Multip

le**,Toolused

forvariousapplications

rangingfrom

mineral

mapping,classificatio

n,end-mem

berselection,

target

detection,

etc.).


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Table

5.Salient

features

ofspectral

matchingtools.

Spectralmatchingtool

Structure

Operatin

gprincipleandfunctio

nsRem

arks

Tetracorder

Fun

ctionalitiessuch

asRatio

spectra,

Mod

ifiedleast-squaresspectral

feature

fitting

,Con

tinuu

mremov

al,Con

straint

andidentificationanalysisareavailable

ascommands

Preprocessing

byCon

tinuu

mremov

alfunctio

n.MRSFF(shape

matching

algo

rithm)used

toidentifyatarget.

Assessm

entof

similarity

ofthetarget

toallcand

idatespectraisthekeyfeature.

After

identification,

‘Group

ing’

functio

ncategorizesthetargets

Improv

ised

versionof

‘Tricorder’for

terrestrialandplanetaryapplications

(Clark

etal.20

03).Sou

rcecode

and

commands

availablein

USGSwebsite.

Highlydepend

enton

thespectral

libraries

(USGS,JPL,andothers)

Spectra

Handlingmod

ule

inPCIGeomatica

The

‘LocalAnalysis’mod

ulecomprises

the

SAM

andSpectra

Handlingtools.

‘Spectralplot’tool

isavailablefor

view

ingthespectra

For

SAM,theou

tput

isaraster.DN

ofpixelscorrespo

ndsto

theanglebetween

thereferenceandtarget

spectra.

‘Spectra

Handling’

tool

allowstheuser

toderive,

conv

olve,andre-format

spectrausing

I2SP,

SPCONVP,

andSP2S

Pfunctio

ns.

‘Spectralplot’im

portsspectrafrom

imageor

spectral

library

andsavesinto

XMLor

SPLlib

rary

ExceptSAM

(hyp

erspectral

classificatio

ntool),mod

ules

exclusiveforspectral

matchingareabsent.29

libraries

availablein

version9.1includ

ingUSGS

andASTER.Metadatacanbe

includ

edforeach

spectrum

usingXMLfiles

SpectralAnalystand

Tactical

Hyp

erspectral

Operatio

nsResou

rce

workflow

(THOR)of

ENVI

‘Mapping

Metho

ds’,‘SpectralAnalyst’,

‘SpectralLibrary

Builder’areavailable

intheSpectraltoolsof

ENVI(5.1).

‘TargetDetectio

n’wizardisavailable.

Alsopresentedin

THORworkflow,an

interactivetool

Con

tinuu

mremov

alandspectral

feature

fitting

possible.Targetspectrum

canbe

matched

with

thereferencespectrausing

thecombinedscoreof

SAM,BE,and

SFF.

‘Library

Builder’canview

,im

port,

andexpo

rtspectral

plot

data.THOR

performsmatchingby

SAM,MF,

ACE,

NED,SSM,SID

,CEM,OSP,

TCIM

F,andUWD.‘M

aterialidentificationtool’

matches

theun

know

nandreference

spectrum

usingSAM.‘H

yperspectral

MaterialIdentificationToo

l’uses

ACE

andthebackgrou

ndstatisticsof

thetarget

spectrum

Stand

ardlib

raries

includ

eASTER

(244

3),

IGCP26

4(139

),USGS(199

4),and

vegetatio

nlib

rary

(99).The

library

can

beim

ported

andexpo

rted

asASCIIand.

sli(nativ

eform

at).Metadata-rich

spectral

library

(MRSL)canbe

createdusing

SpectralLibrary

build

er.Sequential

spectral

library

architectureisconsidered

asalim

itatio

n

(Con

tinued)


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Table

5.(Con

tinued).


Structure

Operatin


nsRem

arks

SpectralAnalysis

Workstatio

nin

ERDASIm

agine

Ano

malydetection,

target

detection,

materialmapping

,andmaterial

identificationareavailableun

derthe

SpectralAnalysttool

Archive

library

windo

w,spectrum

plot,preprocessing,

andmapping

toolsarepresent

Ano

malydetection,

target

detection,

materialmapping,andmaterial

identificationperformed

usingan

inpu

tim

ageandreferencespectraanduser’s

choice

ofmatchingmeasure

(CEM,O

SP,

SAM,andSCM).Pixel

values

ofthe

output

(grey-scaleim

age)

correspo

ndto

thedegree

ofmatch.Preprocessing

,view

ing,

creatio

n,andadditio

nof

spectral

libraries

possible

Spectrallib

raries

ofUSGS,JPL,and

ASTER

areavailable.

Libraries

canalso

becreatedin

thenativ

eform

at(*.spl)of

ERDAS.SPECMIN

spectral

library,

alon

gwith

UserGenerated

Libraries

(UGL),canalso

beaccessed.Autho

rized

userscanutilize

thelib

rary

ofSpectral

Inform

ationTechn

olog

yApp

lication

Center(SITAC)throug

hERDAS

The

SpectralGeologist

(TSG)

Com

prises

Sum

mary,

Log

,Spectrum,

Stack,Scatter,Tray,

HoleFloater,and

Autom

ated

IDscreens.The

Spectral

Assistant

(TSA)andAux

Match

mod

ules

arepresentin

Autom

ated

IDscreen

‘Spectrum

Screen’

canbe

used

toexam

ine

thesing

lespectrum

.‘Log

Screen’

includ

esancillary

data.The

Spectral

Assistant

(TSA)liststhebestmatch

basedon

thecand

idates

intheinbu

iltspectral

library.Mixturesof

mineralsare

also

listed.

Aux

Match

isashape-based

matchingtool

that

uses

spectral

mixtures

asreference

Usedexclusivelyforgeolog

ical

analysis.

Trialversionandpartof

referencelib

rary

availablefordo

wnloading

.Sup

ports

spectral

data

from

variou

sspectroradiometers.Project-specific

libraries

canbe

created,

imported,and

expo

rted

inTSG

form

at

SPECMIN

Com

prises

mod

ules

such

asTables-Search/

Match,Feature

SearchAnalysis(FSA),

andIndexes

FSAprov

ides

theprop

ortio

nsof

thetarget’s

spectral

compo

nents.Table

–Search/

Match

enablesthedisplayof

spectralplot

andits

relatedinform

ation.

‘Ind

exes’

find

sandview

sspecific

spectraalon

gwith

ancillary

data

presentin

thelib

rary

developedby

SpectralInternationalInc.

(201

2)

Includ

esUSGS,JPL,andcustom

ized

libraries.Com

patib

lewith

data

from

any

spectroradiometer.Spectraldata

canbe

expo

rted

into

multip

ledata

form

ats

(Con

tinued)


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Table

5.(Con

tinued).


Structure

Operatin


nsRem

arks

oreXpressDARWin

SpectralData

Acquisitio

nsoftware

Com

prises

EZ-ID

Real-tim

eMineral

Identificationtool,Library

Builder

mod

ule,

androutines

forvegetatio

nindices

Scans

thetarget

spectrum

,allowsselection

ofregion

sof

interestanddisplays

the

bestmatch

basedon

aweigh

tedscore.

Reference

spectraarefrom

USGS,

SPECMIN

,andcustom

libraries.Batch

mod

eprocessing

ofmultip

lespectrais

possible

andresults

canbe

savedon

tospreadsheetsandtext

files.‘Library

Builder’adds

new

targetsto

theexistin

glib

rary.Metadatacanbe

createdor

updatedforeach

entry

Exclusive

softwareforanalysingresults

of‘SpectralEvo

lutio

n’radiom

eters.

Portableversioncanbe

integrated

with

theradiom

eter

during

fieldw

ork.

Inbu

iltmicroph

one,

camera,

andGPSallow

the

collectionof

voiceno

tes,ph

otos,and

locatio

nof

each

target


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standardization and quality assurance methods in the cloud may result in difficulty inassessing the reliability of available data, thereby making it unsuitable for hyperspectraldata users, recent examples like ‘mzCloud’ and the advent of data integrity measuresrender cloud computing a potential option for storing and analysing hyperspectral data.Easy access to spectral resources, automated software deployment, and unlimited storagecapacity through this technology can provide a new dimension to the matching toolsdealing with hyperspectral data sets.

7. Summary

This review has been attempted after realizing the need for an appraisal of past develop-ments, present progress, and future aspects in the domain of spectral matching. Variousstudies, theses, software programs, spectral tools, and libraries were reviewed. Thelimitations and advantages of existing spectral matching approaches were evaluated anddiscussed in this paper. It was realized that spectral matching tools and libraries haveevolved in tandem with advancements in hyperspectral image acquisition and applica-tions. Furthermore, the transition from manual and semi-automatic matching tools, such asUSGS’s Tetracorder and ENVI’s Spectral Analyst, to automated target identification tools,such as ENVI’s Material Identification Workflow and ERDAS’s Spectral AnalysisWorkstation, have resulted in improved accuracy of target detection and material map-ping. Although spectral matching tools individually possess unique capabilities, they havefailed to resolve the issues related to spectral library, absorption features, illuminationeffects, false hits, distortion of spectra, and mixed pixels. Thus, it is inferred that acombination of spectral abilities of two or more algorithms such as SAM-SID mixedmeasure (Du et al. 2004), SID-SCA, (Naresh Kumar et al. 2011), and NS3 (Nidamanuriand Zbell 2011a) yield better matching performance than individual measures. Thisreview has highlighted that most spectral matching libraries and applications haverevolved around mineral mapping, agriculture and forestry, coastal and ocean bathymetry,ocean colour mapping, and planetary material mapping. Thus, the lack of any role in thefields of military applications, urban feature extraction, water quality mapping, andartefact removal is to be addressed. The operating principle and structure of commonlyused spectral matching tools is also reviewed and listed to provide a brief account. Sincecontinuous spectra of target materials form the most essential input of spectral matching,the need for spectral libraries has also been highlighted in this review. Furthermore,various works on collecting spectra, generating and compiling a library, and their usagein matching have been reviewed. In addition, a comprehensive study has been carried outon the method of construction, source of data, querying capability, metadata aspects, andapplications of extensively used libraries such as USGS, ASTER, and RELAB. Theemerging trend of developing a centralized database for uploading and sharing datafrom several sources, viz. SPECCHIO, has also been reported. The need for sensor-specific libraries pertaining to existing hyperspectral missions such as EO-1 Hyperion,CASI, PROBA/CHRIS, and the latest missions for environmental monitoring such asHICO and HyspIRI has been highlighted. The recent concept of ‘cloud’ for compilinglarge volumes of hyperspectral libraries and algorithms for efficient target identificationhas also been cited in this paper. Hence addressing and resolving these issues will increasethe performance of spectral matching in hyperspectral image analysis.


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