This article was downloaded by: [Banaras Hindu University BHU]On: 27 August 2015, At: 12:51Publisher: Taylor & FrancisInforma Ltd Registered in England and Wales Registered Number: 1072954 Registeredoffice: 5 Howick Place, London, SW1P 1WG

Click for updates

International Journal of RemoteSensingPublication details, including instructions for authors andsubscription information:http://www.tandfonline.com/loi/tres20

Comparison of support vector machine,artificial neural network, and spectralangle mapper algorithms for cropclassification using LISS IV dataPradeep Kumara, Dileep Kumar Guptaa, Varun Narayan Mishraa &Rajendra Prasada

a Department of Physics, Indian Institute of Technology (BHU),Varanasi, IndiaPublished online: 16 Mar 2015.

To cite this article: Pradeep Kumar, Dileep Kumar Gupta, Varun Narayan Mishra & Rajendra Prasad(2015) Comparison of support vector machine, artificial neural network, and spectral angle mapperalgorithms for crop classification using LISS IV data, International Journal of Remote Sensing, 36:6,1604-1617, DOI: 10.1080/2150704X.2015.1019015

To link to this article: http://dx.doi.org/10.1080/2150704X.2015.1019015

PLEASE SCROLL DOWN FOR ARTICLE

Taylor & Francis makes every effort to ensure the accuracy of all the information (the“Content”) contained in the publications on our platform. However, Taylor & Francis,our agents, and our licensors make no representations or warranties whatsoever as tothe accuracy, completeness, or suitability for any purpose of the Content. Any opinionsand views expressed in this publication are the opinions and views of the authors,and are not the views of or endorsed by Taylor & Francis. The accuracy of the Contentshould not be relied upon and should be independently verified with primary sourcesof information. Taylor and Francis shall not be liable for any losses, actions, claims,proceedings, demands, costs, expenses, damages, and other liabilities whatsoever orhowsoever caused arising directly or indirectly in connection with, in relation to or arisingout of the use of the Content.

This article may be used for research, teaching, and private study purposes. Anysubstantial or systematic reproduction, redistribution, reselling, loan, sub-licensing,systematic supply, or distribution in any form to anyone is expressly forbidden. Terms &

Conditions of access and use can be found at http://www.tandfonline.com/page/terms-and-conditions

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

Comparison of support vector machine, artificial neural network, andspectral angle mapper algorithms for crop classification using LISS IV

data

Pradeep Kumar*, Dileep Kumar Gupta, Varun Narayan Mishra, and Rajendra Prasad

Department of Physics, Indian Institute of Technology (BHU), Varanasi, India

(Received 14 September 2014; accepted 10 February 2015)

The Resourcesat-2 is a highly suitable satellite for crop classification studies with itsimproved features and capabilities. Data from one of its sensors, the linear imaging andself-scanning (LISS IV), which has a spatial resolution of 5.8 m, was used to comparethe relative accuracies achieved by support vector machine (SVM), artificial neuralnetwork (ANN), and spectral angle mapper (SAM) algorithms for the classification ofvarious crops and non-crop covering a part of Varanasi district, Uttar Pradesh, India.The separability analysis was performed using a transformed divergence (TD) methodbetween categories to assess the quality of training samples. The outcome of thepresent study indicates better performance of SVM and ANN algorithms in compar-ison to SAM for the classification using LISS IV sensor data. The overall accuraciesobtained by SVM and ANN were 93.45% and 92.32%, respectively, whereas the loweraccuracy of 74.99% was achieved using the SAM algorithm through error matrixanalysis. Results derived from SVM, ANN, and SAM classification algorithms werevalidated with the ground truth information acquired by the field visit on the same dayof satellite data acquisition.

1. Introduction

Thematic mapping through image classification algorithms is extensively used in remotesensing applications (Foody 2004). This type of mapping describes the pattern of crops andthe spatial distribution of other land cover features. Accurate crop classification maps derivedfrom remotely sensed data are a prerequisite for analysing many socio-ecological concerns.Thematic mapping is not the easiest task because of somany factors, such as the complexity ofthe landscape, selection of remote-sensing data, image processing, and classification algo-rithms, which may affect the classification accuracy (Lu and Weng 2007). Nowadays, theimagery from recently launched high spatial resolution satellite sensors, such as the LinearImaging Self-Scanner (LISS IV), IKONOS, and QuickBird, is offering new opportunities tothe remote sensing community in the area of agriculture such as crop classification, crop yieldestimation, and effective agricultural planning. The major limitations on crop identificationwith satellite imagery are field to field plant reflectance variability of the same crop as well assimilarity in the plant reflectance of various crops. The potential of the LISS IV sensor hasbeen revealed to capture intrafield variability in crop fields for precise farming (Sesha Sai andNarasimha Rao 2008). Several appropriate algorithms have been developed for the classifica-tion of remote sensing imagery: a review is given by Lu and Weng (2007). Support vectormachine (SVM) and artificial neural network (ANN) algorithms have been widely used in the

*Corresponding author. Email: pradeep.rs.app@itbhu.ac.in

International Journal of Remote Sensing, 2015Vol. 36, No. 6, 1604–1617, http://dx.doi.org/10.1080/2150704X.2015.1019015

© 2015 Taylor & Francis

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

past two decades for image classification (Pal, Maxwell, and Warner 2013). The SVMalgorithm is superior to conventional classifiers and equivalent to disparate learning methodssuch as ANN and decision trees (Huang, Davis, and Townshend 2002). The algorithm ispopular due to its potential for better classification accuracy than ANN (Huang, Davis, andTownshend 2002; Foody and Mathur 2004; Pal, Maxwell, and Warner 2013). The SVMalgorithm is sensitive to the size of training set and dimensionality of the data set (Pal andFoody 2010). It is not based on any assumption regarding the probability distribution of thetraining data sets; instead, it finds a decision directly from the training data in a suitable spacedescribed by a kernel function. The high potential of SVM classification has attracted a greatdeal of research effort. The choice of suitable kernel function, parameters, and strategies forclassification are some of the major concerns in SVM.

Several types of ANN have been developed. Its popularity is basically due to no priorassumptions about the statistics of data (Foody 2004). The back propagation algorithm hasextensively been used for different applications by the remote-sensing community (Massand Flores 2008). Enhanced crop identification and classification have been done forachieving higher accuracy by ANN using QuickBird, Landsat TM/ETM+, and AdvancedVery High Resolution Radiometer (AVHRR) multispectral satellite data (Cruz-Ramírezet al. 2012; Atzberger and Rembold 2013). However, numerous studies have reportedsome problems due to the use of back propagation ANN for crop classification and otherland cover features (Foody and Arora 1997; Kavzoglu and Mather 2003). The classifica-tion accuracy may be affected significantly by the variation in the dimensionality ofremotely sensed data as well as on the training and testing data set (Foody andArora 1997). The spectral angle mapper (SAM) algorithm tests the spectral similaritybetween the endmembers and image pixels or spectral pattern matching between spectralclusters and reference categories. The spectral similarity can also be measured between thespectra in terms of the shape of the spectral pattern using angular distances (Sohn,Morgan, and Gurri 1999). SAM does not require the data to be normally distributed;they are insensitive to data variance and the size of the training data set (Sohn andRebello 2002). Single date multispectral satellite Landsat 5 TM and QuickBird datahave been used for classification by the SAM algorithm (Sohn and Rebello 2002;Yonezawa 2007). The comparative study between SVM and SAM algorithms on SPOT5 and Landsat 5 TM imagery showed that SAM gave a poorer result for crop classification(Alganci et al. 2013). TD is a measure of separability between the two categories. It isbasically used to assess the quality of the statistics prior to image classification tocorrectly classify the categories of interest to improve the digital image classificationaccuracy. These values vary between 0.0 and 2.0 and indicate how well the selectedtraining categories are statistically separated. The classification accuracy increases onincreasing separability between the categories. A TD value greater than 1.9 indicatesbetter separability between the categories. Its values ranging from 1.0 to 1.9 and 0.0 to 1.0indicate moderate and poor separability, respectively (Swain and Davis 1978). Limitedresearch has been conducted on LISS IV sensor data for crop classification studies usingSVM, ANN, and SAM algorithms.

2. Study area and materials

Varanasi (Kashi) district of Uttar Pradesh, India, situated at the bank of the holy River Ganga,is one of the oldest living cities in the world. A part of Varanasi district was selected as thestudy area, extending from 82° 56′ 34″ to 83° 01′ 58″E, and from 25° 12′ 54″ to 25° 15′ 50″N,covering a total area of 6635 ha. The extensive ground truth of the study area, including field

International Journal of Remote Sensing 1605

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

wise information of various crops and non-crop, was collected with the help of theGlobal Positioning System (GPS). The field data collection and image acquisition weredone on 6 April 2013 for our present classification study. High spatial (5.8 m), radiometric(10 bit) and temporal (24 days) resolution data from bands B2 (green, 0.52–0.59 µm), B3 (red,0.62–0.68 µm) and B4 (NIR, 0.77–0.86 µm) of the LISS IV sensor, which has a swath widthof 70 km, were used for the classification. Image processing software Environment forVisualizing Images (ENVI) version 5.1 was used for the comparative analysis of SVM,ANN, and SAM classification algorithms. Figure 1 shows the LISS IV image acquired on 6April 2013. It has been used as input data for all three classifiers.

3. Methodology

The following methodology was adopted in the processing of LISS IV sensor data used inthe study. (1) Layer stacking of green, red, and NIR bands to generate a false colourcomposite (FCC) image. (2) Geometric correction. (3) Subsetting of the study area. (4)Defining training areas and generation of region of interest (ROI) files. The ROI files wereproduced by ENVI software for different categories using ground reference data acquiredfrom the field observations. One of these ROI files was used as training data and other astesting data. (5) Pairwise interclass separability analysis using the TD method. (6)Supervised classification. (7) In total, 15,810 pixels were selected as the training dataset, whereas 5270 pixels were used as the test data set for all the classification algorithms.The supervised algorithms performed for the classification are described as follows.

3.1. SVM classification

The SVM is a supervised non-parametric classification algorithm based on statisticallearning theory. It provides some system-inherent advantages in comparison to other

India

Uttar Pradesh

Varanasi

83˚0′ 0″ E

83˚0′ 0″ E

25˚1

5′ 0

″ N

25˚1

5′ 0

″ N

N

Figure 1. Geographical location of the study area as viewed in a LISS IV sensor image on 6 April,2013.

1606 P. Kumar et al.

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

classification algorithms. SVM yields good classification results from complex and noisydata. It uses a structural risk minimization principle to minimize the probability ofmisclassifying previously unseen data (Vapnik 1998). SVM separates the categories byfitting an optimal linear separating hyperplane between the categories within a multi-dimensional feature space. The designed algorithm maximizes the margin between theoptimal linear separating hyperplane and the closest training samples – called supportvectors (Vapnik 1998; Huang, Davis, and Townshend 2002). The most useful trainingcases are those which lie close to where the hyperplane is to be fitted. For the non-lineardata classification into higher dimensional Hilbert feature space, an optimal non-linearseparating hyperplane can be fitted to a more complex class distribution, which is notseparable in the original feature space (Vapnik 1998). The gamma parameter was set to avalue equal to the inverse of the number of spectral bands of the LISS IV data (i.e. 0.33),whereas the penalty parameter was set to a value of 100, forcing no misclassificationduring the training process. The value of pyramid parameter was taken to be zero toprocess the LISS IV data at full resolution by ENVI. The zero classification probabilitythreshold parameter indicates that all the image pixels had to be classified into onecategory. The most suitable radial basis function (RBF) kernel was used in the presentstudy because it requires less computational work. The RBF kernel is mostly applicablewhen the relationship between training samples and the entire data set is non-linear. Thus,it can be used effectively for the heterogeneous areas (Su et al. 2007).

3.2. ANN classification

ANN is a mathematical algorithm inspired by the human brain (Mas and Flores 2008).The importance of ANN is because of its capability adaptively to simulate non-linear andcomplex patterns with appropriate topological structures (Atkinson and Tatnall 1997). Thetopological structure of the ANN used in satellite image classification consists of a neuronin the input layer representing one of the input features such as one satellite image band,whereas each neuron in the output layer corresponds to one of the classes (Srivastavaet al. 2012). The neural network classifier used is a layered feed forward model in ENVIversion 5.1. The network layers consist of an input layer, an output layer, and one or morehidden layers. It uses standard back propagation for supervised learning. An iterationgradient algorithm of back propagation was used to minimize the root mean square error(RMSE) between the actual output of a multilayer feed forward ANN and the desiredoutput. The iteration process was stopped when the RMSE reached the optimum level. Inthe present study, the ANN contains 1 input layer with 3 neurons, 1 hidden layer and 13neurons in the output layer as classification categories. The learning rate and momentumvalue were taken as 0.10 and 0.90, respectively, for the processing of data set by ANN.Other parameters namely RMSE and numbers of training iterations were taken as 0.01 and1000, respectively, for the classification.

3.3. SAM classification

SAM uses a classification decision rule based on spectral angles formed between areference spectrum and unclassified pixel in n-dimensional space, where n representsthe number of spectral bands. A vector is plotted in n-dimensional space from the origin toan unknown pixel (Sohn, Morgan, and Gurri 1999). A comparison between spectralangles of reference spectra and an image pixel is made, and each pixel is assigned to aclass that has the smallest spectral angle (Sohn and Rebello 2002). The performance of the

International Journal of Remote Sensing 1607

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

SAM algorithm increases on increasing vector dimension, which is possible on increasingthe number of spectral bands (Girouard et al. 2004; Alganci et al. 2013). For this reason,SAM studies are focused mostly on hyperspectral satellite data for classification (Clark,Roberts, and Clark 2005). The performance of a classifier depends on several factors.However, the number of spectral bands is one of the factors responsible for the betterperformance of a classifier.

4. Results and discussion

Multiple crops such as wheat, corn, linseed, lentil, mustard, barley, and other cropsgrown in the study area were classified. Wheat was identified as the dominant crop outof the seven crop categories used. The other crops category contains gram, sugarcane,pigeon pea, black gram, green gram, oat, pea, vegetables, etc. Six other land covertypes, namely water, sparse vegetation, dense vegetation, fallow land, built up, and sandwere also used in the classification. The classification accuracies were assessed byenumerating overall accuracy (OA), user’s accuracy (UA), producer’s accuracy (PA),and kappa statistic (k) from error matrix analysis for each category. The TD value 1.80was considered as the limit for best discrimination of different category pairs (Sesha Saiand Narasimha Rao 2008). The TD values were found to be greater than 1.80 for 73pairs, which showed better separability for these pairs of categories, leading to higherclassification accuracy. TD values less than 1.80 were observed only for five pairs ofcategories. The TD values of spectrally confusing categories are presented in Table 1.The classification maps produced by SVM, ANN, and SAM algorithms are shown inFigures 2, 3, and 4, respectively.

Table 2 summarizes the classification accuracy assessment results obtained by theSVM classification algorithm. The highest OA (93.45%) was achieved by SVM incomparison to ANN and SAM classification algorithms. The UA is a measure of thecommission error and indicates the probability that a pixel classified into a givencategory is actually represented by that category on the ground (Lillesand andKiefer 1999). It ranged from 58% to 97% in crop categories and 88% to 100% innon-crop categories. The PA is the measure of omission error, which indicates how wellthe training set pixels of the given category are classified. It ranged from 47% to 98% incrop categories and 54% to 100% in non-crop categories. The 70.13% PA and 87.10%UA for corn indicate that 70.13% of the corn areas were correctly identified as corn,whereas 87.10% of the corn areas were actually corn in the classified map. In the caseof linseed crop, the UA was quite good (73.96%), whereas its PA was found to be only47.33%. The producer of the classification can reasonably claim that 47.33% of the timean area that was linseed was identified as such. Whereas the user of the classificationwould find that 73.96% of the time an area visited on the ground that the classificationsays is linseed will actually be linseed. The PA (53.69%) and UA (98.50%) for built upindicate that 53.69% of the built up areas were correctly identified as built up, whereas98.50% of the areas in the classified map were actually built up. Wheat was the easiestcategory to identify, while linseed and other crops were most difficult to differentiateamong the seven crop categories. The low OA and high omission error of linseed wasmostly due to confusion and spectral similarities with sparse vegetation, lentil, mustardand other crops. However, the linseed, mustard, wheat, and sparse vegetation weremisclassified as other crops due to spectral similarities, which led to high omissionand commission errors for the other crops category. Due to these spectral similaritiesand mixing, similar results were found for linseed and other crops using SVM, ANN,

1608 P. Kumar et al.

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

Table

1.Transform

eddivergence

(TD)forvariou

sconfusioncatego

rypairswith

LISSIV

sensor

data.

Class

pair

TD

Class

pair

TD

Class

pair

TD

Class

pair

TD

Class

pair

TD

Corn–

linseed

1.95

57Linseed–o

ther

crop

s1.70

32Lentil–sand

1.99

99Barley–

built

up2.00

00Water–d

ense

vegetatio

n2.00

00

Corn–

lentil

1.99

99Linseed–w

ater

2.00

00Mustard–b

arley

1.99

55Barley–

sand

2.00

00Water–fallow

land

2.00

00Corn–

mustard

1.99

95Linseed–sparse

vegetatio

n1.45

86Mustard–w

heat

1.97

86Wheat–o

ther

crop

s1.78

52Water–b

uiltup

2.00

00

Corn–

barley

2.00

00Linseed–d

ense

vegetatio

n1.99

99Mustard–o

ther

crop

s1.84

68Wheat–w

ater

2.00

00Water–sand

2.00

00

Corn–

wheat

1.99

99Linseed–fallow

land

1.99

69Mustard–w

ater

2.00

00Wheat–sparse

vegetatio

n1.99

86Sparsevegetatio

n–densevegetatio

n1.99

61

Corn–

other

crop

s1.93

19Linseed–b

uilt

up1.99

97Mustard–sparse

vegetatio

n1.99

59Wheat–d

ense

vegetatio

n2.00

00Sparsevegetatio

n–fallo

wland

1.99

68

Corn–

water

2.00

00Linseed–sand

2.00

00Mustard–d

ense

vegetatio

n2.00

00Wheat–fallow

land

2.00

00Sparsevegetatio

n–bu

iltup

1.99

99

Corn–

sparse

vegetatio

n1.82

62Lentil–m

ustard

1.9978

Mustard–fallow

land

2.00

00Wheat–b

uiltup

1.99

99Sparsevegetatio

n–sand

2.00

00

Corn–

dense

vegetatio

n1.96

80Lentil–b

arley

1.91

95Mustard–b

uilt

up2.00

00Wheat–sand

2.00

00Dense

vegetatio

n–fallo

wland

2.00

00

Corn–

fallo

wland

2.00

00Lentil–w

heat

1.93

29Mustard–sand

2.00

00Other

crop

s–water

2.00

00Dense

vegetatio

n–bu

iltup

2.00

00

Corn–

built

up2.00

00Lentil–o

ther

crop

s1.98

97Barley–

wheat

1.99

99Other

crop

s–sparse

vegetatio

n1.79

90Dense

vegetatio

n–sand

2.00

00

Corn–

sand

2.00

00Lentil–w

ater

2.00

00Barley–

other

crop

s1.99

93Other

crop

s–dense

vegetatio

n1.99

75Fallow

land

–builtup

1.99

99

Linseed–lentil

1.90

78Lentil–sparse

vegetatio

n1.99

99Barley–

water

2.00

00Other

crop

s–fallo

wland

2.00

00Fallow

land

–sand

2.00

00

Linseed–

mustard

1.69

68Lentil–d

ense

vegetatio

n2.00

00Barley–

sparse

vegetatio

n1.99

99Other

crop

s–bu

iltup

1.99

99Builtup–sand

1.99

99

Linseed–

barley

1.98

75Lentil–fallow

land

1.99

99Barley–

dense

vegetatio

n2.00

00Other

crop

s–sand

2.00

00

Linseed–

wheat

1.90

68Lentil–b

uiltup

1.99

99Barley–

fallo

wland

2.00

00Water–sparse

vegetatio

n2.00

00

International Journal of Remote Sensing 1609

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

Corn

Linseed

Lentil

Mustard

Barley

Wheat

Other crops

Water

Sparse vegetation

Dense vegetation

Fallow land

Built up

Sand

km4210N

83˚0′ 0″ E

83˚0′ 0″ E

25˚1

5′ 0

″ N

25˚1

5′ 0

″ N

Figure 2. SVM algorithm-based classified map.

Corn

Linseed

Lentil

Mustard

Barley

Wheat

Other crops

Water

Sparse vegetation

Dense vegetation

Fallow land

Built up

Sand

83˚0′ 0″ E

83˚0′ 0″ E

25˚1

5′ 0

″ N

25˚1

5′ 0

″ N

km4210N

Figure 3. ANN algorithm-based classified map.

1610 P. Kumar et al.

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

and SAM algorithms. The similar patterns of PA and UA observed for lentil, mustard,barley, wheat, and other crops indicate that they were correctly identified on the groundas well as actually classified on the map. Similarly, it happened for non-crop categories,excluding the built up category. In the crop categories, the accuracy of lentil crop wasfound to be higher than corn, linseed, mustard, barley, and other crops, whereas it wasfound to be lower than wheat crop. Similar accuracy patterns were found by the ANNand SAM classification algorithms. In the ground validation using the SVM classifica-tion algorithm, 4925 pixels out of 5270 test pixels were found to be correct. The k is ameasure of agreement that compares the observed agreement to agreement expected bychance (Cohen 1960). The value of k was 0.9216, indicating an achieved classificationaccuracy that was 92% better than the random assignment of pixels to categories.

Table 3 summarizes the classification accuracy assessment results obtained by theANN classification algorithm. The second highest OA (92.32%) was achieved by theANN algorithm. Similar accuracy results were found using the ANN classification algo-rithm by ERS-1 and QuickBird data for crops and non-crop categories in India(Chakraborty and Panigrahy 1997; Omkar et al. 2008). This algorithm is time-consumingbecause a number of networks must be trained for comparative purposes. One of theadvantages of ANN is its non-parametric nature, and it can be used for more complexoperations. They are data-dependent and learn the characteristics of training data typicallyin an iterative way, which has a direct effect on classification accuracy (Kavzoglu andMather 1999). The ANN algorithm does not require implementation of a multiclassstrategy for the classification accuracy (Pal, Maxwell, and Warner 2013). The variationin UA was found to range from 48% to 94% for the crop categories, and 87% to 100% in

Corn

Linseed

Lentil

Mustard

Barley

Wheat

Other crops

Water

Sparse vegetation

Dense vegetation

Fallow land

Built up

Sand

km4210N

83˚0′ 0″ E

83˚0′ 0″ E

25˚1

5′ 0

″ N

25˚1

5′ 0

″ N

Figure 4. SAM algorithm-based classified map.

International Journal of Remote Sensing 1611

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

Table

2.Con

fusion

matrixprod

uced

usingtheSVM

classificatio

nalgo

rithm

fortheLISSIV

sensor

data.

Class

Corn

Linseed

Lentil

Mustard

Barley

Wheat

Other

crop

sWater

Sparse

vegetatio

nDense

vegetatio

nFallow

land

Built

upSand

Row

total

Com

mission

error(%

)UA

(%)

Corn

548

6212

.90

87.10

Linseed

713

34

211

296

26.04

73.96

Lentil

717

118

1320

918

.18

81.82

Mustard

111

732

1198

25.51

74.49

Barley

2045

166

31.82

68.18

Wheat

134

97

436

13.32

96.68

Other

crop

s12

47

4711

8141

.98

58.02

Water

1283

1283

0.00

100.00

Sparse

vegetatio

n18

4910

460

868

911.76

88.24

Dense

vegetatio

n5

540

545

0.92

99.08

Fallow

land

207

721

43.27

96.73

Builtup

213

113

31.50

98.50

Sand

8713

4614

336.07

93.93

Colum

ntotal

7715

019

890

6735

872

1283

630

548

207

244

1346

5270

Omission

error

(%)

29.87

52.67

13.64

18.89

32.84

2.51

34.72

0.00

3.49

1.46

0.00

46.31

0.00

Produ

cer’s

accuracy

(%)70

.13

47.33

86.36

81.11

67.16

97.49

65.28

100.00

96.51

98.54

100.00

53.69

100.00

Note:

OA

=49

25/527

0=93

.45%

,kapp

acoefficient=0.9216

.

1612 P. Kumar et al.

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

Table

3.Con

fusion

matrixprod

uced

usingtheANN

classificatio

nalgo

rithm

fortheLISSIV

sensor

data.

Class

Corn

Linseed

Lentil

Mustard

Barley

Wheat

Other

crop

sWater

Sparse

vegetatio

nDense

vegetatio

nFallow

land

Built

upSand

Row

total

Com

mission

error(%

)UA

(%)

Corn

495

14

5916

.95

83.05

Linseed

5914

131

188

32.95

67.05

Lentil

2318

65

517

236

21.19

78.81

Mustard

102

851

714

119

28.57

71.43

Barley

614

121

33.33

66.67

Wheat

235

517

537

96.33

93.67

Other

crop

s8

126

1954

51.85

48.15

Water

1283

1283

00.00

100.00

Sparse

vegetatio

n31

432

358

62

367

012

.54

87.46

Dense

vegetatio

n18

541

559

3.22

96.78

Fallow

land

204

1622

07.27

92.73

Builtup

27

24

131

146

10.27

89.73

Sand

9013

4614

366.27

93.73

Colum

ntotal

9814

321

510

426

362

5912

8362

554

820

725

413

4652

70Omission

error(%

)50

.00

58.74

13.49

18.27

46.15

1.93

55.93

00.00

6.24

1.28

1.45

48.43

00.00

PA(%

)50

.00

41.26

86.51

81.73

53.85

98.07

44.07

100.00

93.76

98.72

98.55

51.57

100.00

Note:

OA

=4865/5270=92.32%

,kappacoefficient=0.9124.

International Journal of Remote Sensing 1613

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

the case of non-crop categories. Similarly, PA ranged from 41% to 98% in crop categories,and 51% to 100% in the case of non-crop categories. The values of PA for lentil, mustard,wheat and dense vegetation categories were found to be higher, whereas PA of all othercategories, including UA, was found to be lower by the ANN in comparison to SVMalgorithm. Thus, wheat was the easiest category to identify, whereas the other cropscategory was most difficult to differentiate among crop categories using ANN. Some ofthe categories such as linseed, wheat, other crops, and sparse vegetation were misclassi-fied as mustard due to spectral similarities and variability within the field. In total, 4865pixels out of 5270 test pixels were found to be correct in the validation. The value of kestimated by the ANN classification algorithm was 0.9124. The visual comparison ofthematic maps and statistical results inferred that the SVM and ANN algorithms per-formed well and provided fairly good separations among crops and other land coverfeatures, in spite of few crop fields containing one or more crops. However, the majorityof fields contained only a single crop in the study area. One of the reasons for more than90% accuracies achieved by the SVM and ANN algorithms was the higher classificationaccuracies of non-crop categories. Higher classification accuracies of non-crop categoriessignify that there was no or less mixing with some of other categories due to its differentspectral response. The performance of SVM was found to be better than ANN usingEnvisat ASAR, ERS, Landsat 5 TM, and SPOT 5 multi temporal satellite data (Waske andBenediktsson 2007). One of the disadvantages of the SVM algorithm is the salt andpepper effect. It is due to the inherent spectral variability encountered by the classifierwhen applied on a pixel-by-pixel basis (Lillesand and Kiefer 1999). This salt and peppernoise is also a disadvantage of the ANN and SAM algorithms.

Table 4 summarizes the classification accuracy assessment results obtained by theSAM classification algorithm. The OA achieved was 74.99% of the classified map, whichwas found to be lowest in comparison to the SVM and ANN algorithms. The variation inUAwas found to range from 13% to 75% for the crop categories, and 51% to 100% in thecase of non-crop categories. Similarly, the variation in PAwas found to range from 41% to82% for crop categories, and 18% to 100% in the case of non-crop categories. The PA ofcorn, barley and UA of dense vegetation categories were found higher, while all othercategories were found to be lower by SAM in comparison to the SVM and ANNclassification algorithms. Again, wheat was the easiest crop category to identify, whereasthe lowest OAwas achieved for linseed crop by the SAM algorithm. The low UA for cornindicates very small angular variation between corn and the sparse and dense vegetation,which was not found in reality. The same thing happened between linseed and sparsevegetation. This small angular variation was only due to spectral similarities between thecategories. The value of k estimated by the SAM algorithm was 0.7062. In total, 3952pixels were found to be correct out of the 5270 test pixels present in the validation usingthe SAM algorithm. Lower accuracy was produced using the SAM algorithm. It meansthat the SAM algorithm underperformed in comparison to the SVM and ANN algorithmsdue to a lower number of spectral bands with LISS IV sensor data.

The highest OA for wheat crop indicates less mixing or unmixing with the other cropcategories. Whereas 100% OA, found only for water by the SVM, ANN, and SAMalgorithms, indicates unmixing of other categories with the water. Small portions of corn,linseed, mustard, and other crops categories were misclassified as sparse vegetation. Therewas some mixing of pixels found between sand and built up categories due to similarreflectance values. The commission and omission errors were due to spectral similaritiesbetween various categories. The 0% commission error for water and 0% omission errorsfor water, sand, and fallow land show no confusion for the classification between different

1614 P. Kumar et al.

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

Table

4.Con

fusion

matrixprod

uced

usingtheSAM

classificatio

nalgo

rithm

fortheLISSIV

sensor

data.

Class

Corn

Linseed

Lentil

Mustard

Barley

Wheat

Other

crop

sWater

Sparse

vegetatio

nDense

vegetatio

nFallow

land

Built

upSand

Row

total

Com

mission

error(%

)UA

(%)

Corn

631

206

122

392

83.93

16.07

Linseed

4932

94

264

936

786

.65

13.35

Lentil

153

102

6221

248

38.31

61.69

Mustard

221

4160

4213

179

77.09

22.91

Barley

2247

335

6113

58.41

41.59

Wheat

197

156

234

1612

331

225

.00

75.00

Other

crop

s3

216

321

256

42.86

57.14

Water

1283

1283

0.00

100.00

Sparse

vegetatio

n11

164

160

119

216

.67

83.33

Dense

vegetatio

n3

425

428

0.70

99.30

Fallow

land

815

44

111

915

126

.49

73.51

Builtup

120

112

232

48.28

51.72

Sand

8312

3413

176.30

93.70

Colum

ntotal

77117

198

9067

358

6212

8367

354

820

724

413

4652

70Omission

error(%

)18

.18

58.12

22.73

54.44

29.85

34.64

48.39

0.00

76.23

22.45

46.38

50.82

8.32

PA(%

)81

.82

41.88

77.27

45.56

70.15

65.36

51.61

100.00

23.77

77.55

53.62

49.18

91.68

Note:

OA

=3952/5270=74.99%

,kappacoefficient=0.7062.

International Journal of Remote Sensing 1615

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

categories using the SVM, ANN, and SAM algorithms. The classification accuracies ofmore than 90% achieved by SVM and ANN show high agreement; however, the lowerOA (74.99%) achieved by the SAM classification algorithm shows fair agreementbetween the classified remote-sensing data and ground truth data.

5. Conclusion

The aim of the present study was to evaluate the performance of the SVM, ANN, andSAM algorithms for the classification of various crops using LISS IV sensor data. TDvalues greater than 1.80, found for almost all the categories, show better separability,leading to high classification accuracy. Results obtained by the SVM and ANN algorithmssuggest that both the classification algorithms have high potential, whereas the SAMalgorithm has shown low potential to achieve better classification accuracy using LISS IVsensor data. The LISS IV sensor, besides maintaining the continuity in the data avail-ability, provides improved quality of data for crop classification studies.

Disclosure statementNo potential conflict of interest was reported by the authors.

FundingThe authors wish to gratefully acknowledge Prof. Rajeev Sangal, Director, Indian Institute ofTechnology (BHU), Varanasi, for the financial support to procure software ENVI version 5.1 withthe SARscape module.

ReferencesAlganci, U., E. Sertel, M. Ozdogan, and C. Ormeci. 2013. “Parcel Level Identification of Crop

Types Using Different Classification Algorithms and Multi-Resolution Imagery in SouthEastern Turkey.” Photogrammetric Engineering and Remote Sensing 79: 1053–1065.doi:10.14358/PERS.79.11.1053.

Atkinson, P. M., and A. R. L. Tatnall. 1997. “Introduction Neural Networks in Remote Sensing.”International Journal of Remote Sensing 18: 699–709. doi:10.1080/014311697218700.

Atzberger, C., and F. Rembold. 2013. “Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets.” Remote Sensing 5: 1335–1354. doi:10.3390/rs5031335.

Chakraborty, M., and S. Panigrahy. 1997. “Comparative Performance of Per-Pixel Classifiers UsingERS-1 SAR Data for Classification of Rice Crop.” Journal of the Indian Society of RemoteSensing 25: 155–161. doi:10.1007/BF03024216.

Clark, M. L., D. A. Roberts, and D. B. Clark. 2005. “Hyperspectral Discrimination of Tropical RainForest Tree Species at Leaf to Crown Scales.” Remote Sensing of Environment 96: 375–398.doi:10.1016/j.rse.2005.03.009.

Cohen, J. 1960. “A Coefficient of Agreement for Nominal Scales.” Educational and PsychologicalMeasurement 20: 37–46. doi:10.1177/001316446002000104.

Cruz-Ramírez, M., C. Hervás-Martínez, M. Jurado-Expósito, and F. López-Granados. 2012. “AMulti-Objective Neural Network Based Method for Cover Crop Identification from RemoteSensed Data.” Expert Systems with Applications 39: 10038–10048. doi:10.1016/j.eswa.2012.02.046.

Foody, G. M. 2004. “Thematic Map Comparison: Evaluating the Statistical Significance ofDifferences in Classification Accuracy.” Photogrammetric Engineering and Remote Sensing70: 627–633. doi:10.14358/PERS.70.5.627.

1616 P. Kumar et al.

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015

Foody, G. M., and M. K. Arora. 1997. “An Evaluation of Some Factors Affecting the Accuracy ofClassification by an Artificial Neural Network.” International Journal of Remote Sensing 18:799–810. doi:10.1080/014311697218764.

Foody, G. M., and A. Mathur. 2004. “A Relative Evaluation of Multiclass Image Classification bySupport Vector Machines.” IEEE Transactions on Geoscience and Remote Sensing 42: 1335–1343. doi:10.1109/TGRS.2004.827257.

Girouard, G., A. Bannari, A. El Harti, and A. Desrochers. 2004. “Validated Spectral Angle MapperAlgorithm for Geological Mapping: Comparative Study between Quickbird and Landsat-TM.”www.isprs.org/istambul 2004/comm4/papers/432 pdf

Huang, C., L. S. Davis, and J. R. G. Townshend. 2002. “An Assessment of Support VectorMachines for Land Cover Classification.” International Journal of Remote Sensing 23: 725–749. doi:10.1080/01431160110040323.

Kavzoglu, T., and P. M. Mather. 1999. “Pruning Artificial Neural Networks: An Example UsingLand Cover Classification of Multi-Sensor Images.” International Journal of Remote Sensing20: 2787–2803. doi:10.1080/014311699211796.

Kavzoglu, T., and P. M. Mather. 2003. “The Use of Back Propagating Artificial Neural Networks inLand Cover Classification.” International Journal of Remote Sensing 24: 4907–4938.doi:10.1080/0143116031000114851.

Lillesand, T. M., and R. W. Kiefer. 1999. Remote Sensing and Image Interpretation. New York:Wiley.

Lu, D., and Q. Weng. 2007. “A Survey of Image Classification Methods and Techniques forImproving Classification Performance.” International Journal of Remote Sensing 28: 823–870. doi:10.1080/01431160600746456.

Mas, J. F., and J. J. Flores. 2008. “The Application of Artificial Neural Networks to the Analysis ofRemotely Sensed Data.” International Journal of Remote Sensing 29: 617–663. doi:10.1080/01431160701352154.

Omkar, S. N., J. Senthilnath, D. Mudigere, and M. M. Kumar. 2008. “Crop Classification UsingBiologically Inspired Techniques with High Resolution Satellite Image.” Journal of the IndianSociety of Remote Sensing 36: 175–182. doi:10.1007/s12524-008-0018-y.

Pal, M., and G. M. Foody. 2010. “Feature Selection for Classification of Hyperspectral Data bySVM.” IEEE Transactions on Geoscience and Remote Sensing 48: 2297–2307. doi:10.1109/TGRS.2009.2039484.

Pal, M., A. E. Maxwell, and T. A. Warner. 2013. “Kernel-Based Extreme Learning Machine forRemote-Sensing Image Classification.” Remote Sensing Letters 4: 853–862. doi:10.1080/2150704X.2013.805279.

Sesha Sai, M. V. R., and P. V. Narasimha Rao. 2008. “Utilization of Resourcesat-1 Data forImproved Crop Discrimination.” International Journal of Applied Earth Observation andGeoinformation 10: 206–210. doi:10.1016/j.jag.2008.02.009.

Sohn, Y., E. Morgan, and F. Gurri. 1999. “Deforestation in North-Central Yucatan (1985–1995):Mapping Secondary Succession of Forest and Agricultural Land Use in Sotuta Using the Cosineof the Angle Concept.” Photogrammetric Engineering and Remote Sensing 65: 947–958.

Sohn, Y., and N. S. Rebello. 2002. “Supervised and Unsupervised Spectral Angle Classifiers.”Photogrammetric Engineering and Remote Sensing 68: 1271–1280.

Srivastava, P. K., D. Han, M. A. Rico-Ramirez, M. Bray, and T. Islam. 2012. “Selection ofClassification Techniques for Land Use/Land Cover Change Investigation.” Advances inSpace Research 50: 1250–1265. doi:10.1016/j.asr.2012.06.032.

Su, L., M. J. Chopping, A. Rango, J. V. Martonchik, and D. P. C. Peters. 2007. “Support VectorMachines for Recognition of Semi-Arid Vegetation Types Using MISR Multi-Angle Imagery.”Remote Sensing of Environment 107: 299–311. doi:10.1016/j.rse.2006.05.023.

Swain, P. H., and S. M. Davis. 1978. Remote Sensing: The Quantitative Approach. New York:McGraw Hill Book Company.

Vapnik, V. N. 1998. Statistical Learning Theory. New York: Wiley.Waske, B., and J. A. Benediktsson. 2007. “Fusion of Support Vector Machines for Classification of

Multisensor Data.” IEEE Transactions on Geoscience and Remote Sensing 45: 3858–3866.doi:10.1109/TGRS.2007.898446.

Yonezawa, C. 2007. “Maximum Likelihood Classification Combined with Spectral Angle MapperAlgorithm for High Resolution Satellite Imagery.” International Journal of Remote Sensing 28:3729–3737. doi:10.1080/01431160701373713.

International Journal of Remote Sensing 1617

Dow

nloa

ded

by [

Ban

aras

Hin

du U

nive

rsity

BH

U]

at 1

2:51

27

Aug

ust 2

015