Processing, mosaicking and management of the Monterey Baydigital sidescan-sonar images

Pat S. Chavez Jr. a;*, JoAnn Isbrecht a, Peter Galanis b, Gregory L. Gabel b,Stuart C. Sides a, Deborah L. Soltesz a, Stephanie L. Ross b,

Miguel G. Velasco a

a U.S. Geological Survey, 2255 N. Gemini Drive, Flagsta¡, AZ 86001, USAb U.S. Geological Survey, 345 Middle¢eld Rd, Menlo Park, CA 94025, USA

Received 24 November 1999; accepted 15 July 2001

Abstract

Sidescan-sonar imaging systems with digital capabilities have now been available for approximately 20 years. Inthis paper we present several of the various digital image processing techniques developed by the U.S. GeologicalSurvey (USGS) and used to apply intensity/radiometric and geometric corrections, as well as enhance and digitallymosaic, sidescan-sonar images of the Monterey Bay region. New software run by a WWW server was designed andimplemented to allow very large image data sets, such as the digital mosaic, to be easily viewed interactively, includingthe ability to roam throughout the digital mosaic at the web site in either compressed or full 1-m resolution. Theprocessing is separated into the two different stages: preprocessing and information extraction. In the preprocessingstage, sensor-specific algorithms are applied to correct for both geometric and intensity/radiometric distortionsintroduced by the sensor. This is followed by digital mosaicking of the track-line strips into quadrangle format whichcan be used as input to either visual or digital image analysis and interpretation. An automatic seam removal procedurewas used in combination with an interactive digital feathering/stenciling procedure to help minimize tone or seammatching problems between image strips from adjacent track-lines. The sidescan-sonar image processing package is partof the USGS Mini Image Processing System (MIPS) and has been designed to process data collected by any `generic'digital sidescan-sonar imaging system. The USGS MIPS software, developed over the last 20 years as a public domainpackage, is available on the WWW at: http://terraweb.wr.usgs.gov/trs/software.html. Published by Elsevier ScienceB.V.

Keywords: Sidescan; Sonar; Image enhancement; Remote sensing; Sea£oor mapping; Monterey Bay

1. Introduction

The use of digital data collected with remotesensing instruments has increased rapidly duringthe last two decades. One of the members of theremote sensing family of digital imaging systemsis the sidescan-sonar system; it is an active system

0025-3227 / 02 / $ ^ see front matter Published by Elsevier Science B.V.PII: S 0 0 2 5 - 3 2 2 7 ( 0 1 ) 0 0 2 7 3 - 0

* Corresponding author. Tel. : +1-520-556-7221;Fax: +1-520-556-7169.

E-mail address: pchavez@terraweb.wr.usgs.gov(P.S. Chavez Jr.).

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www.elsevier.com/locate/margeo

using acoustic waves to produce images of thesea£oor, called sonographs (Sutton, 1979). Thesonographs are a measure of the backscatterproperties of the sea£oor. This paper discussesvarious digital image processing techniques devel-oped and re¢ned by the United States GeologicalSurvey (USGS) over the past 15 years and used tocorrect and mosaic the digital sidescan-sonar im-ages covering portions of Monterey Bay. As partof the USGS Monterey Bay National MarineSanctuary project, a web-based image navigationtool was created which can display a very largedigital image, making it widely and easily avail-able to others using the World Wide Web(WWW).

Development of a sidescan-sonar processingpackage by the USGS started in 1979 using theexpertise developed to process and analyze side-looking airborne radar. The sidescan processingpackage has been used by the USGS in an opera-tional mode since the early 1980s (Teleki et al.,1981; Chavez, 1986), with additions and improve-ments made as needed for some of the varioushigher-resolution imaging systems that have beencoming on line during the last decade. The pack-age includes digital mosaicking capabilities and isdesigned to handle data from any generic sonarimaging system. The sidescan-sonar image pro-cessing package corrects for both intensity/radio-metric and geometric distortions and is part of theUSGS Mini Image Processing System (MIPS),which also includes both general and multispec-tral image processing capabilities (Chavez, 1984).The USGS MIPS software is a public domainpackage and available on the WWW at http://terraweb.wr.usgs.gov/trs/software.html. The ob-jective of this paper is to describe the core pro-cessing and manipulating capabilities used to gen-erate the digital mosaics using the sidescan-sonarimage data covering portions of Monterey Bayand to discuss the use of the WWW as a datavisualization and distribution tool to make the1-m resolution digital mosaic easily available toothers.

The operational core processing capabilitiesused on the sidescan-sonar image data havebeen rather stable since about 1985, with someenhancements and improvements made to the se-

quence and algorithms used. Because the softwareand procedures have been relatively stable forover 15 years, some of the information presentedin this paper duplicates information presented inprevious publications (Chavez and Gardner,1994; Chavez, 1986; Chavez et al., 1996). Thesidescan processing package within the USGSMIPS has been used in an operational mode togenerate various products and several atlases ofthe Exclusive Economic Zone using GLORIAsidescan-sonar image data (EEZ-SCAN, 1986,1987; EEZ, 1991 ^ these are published as USGSMiscellaneous Investigations Series).

2. Data characteristics and test site

Sidescan-sonar images have been used to ex-tract information about the sea£oor for over 40years (Rusby, 1970; Flemming, 1976; Rusby andSomers, 1977). In order to characterize the sea-£oor geology of the Monterey Bay continentalshelf the USGS used sidescan-sonar to acousti-cally swath map the region from nearshore, atabout 10^15 m water depth, to the shelf breakat 120 m depth. The image data are being usedto help map the sea£oor geology (Eittreim et al.,2002), which in turn can help with understandingthe myriad of biological habitats, locations ofsediment erosion and deposition and sedimenttransport through the Monterey Bay NationalMarine Sanctuary region.

The mosaic of the sidescan-sonar images (orsound-photos) of the sea£oor covering the north-ern Monterey Bay shelf region was made by dig-itally mosaicking together a large number of 200^500-m-wide image strips collected along parallelship tracks. In northern Monterey Bay the towedsidescan-sonar imaging systems used were aKlein1 100- and an EGpG 59-kHz ¢sh (Fig. 1).In southern Monterey Bay hull-mounted multi-beam swath imaging systems (Simrad EM-1000and EM-300) were used; these systems collect

1 Use of trade names and trademarks in this publication isfor identi¢cation purposes only and does not constitute en-dorsement by the U.S. Geological Survey.

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both backscatter data, similar to sidescan-sonarsystems, and bathymetric data. Two of the mainparameters a¡ecting backscatter are the rough-ness/texture and density of the surface covertype (i.e., rock, sand, or biologic type on the sea-£oor). Bright tones in the mosaic represent areaswith high backscatter, while dark tones representareas with low backscatter. The near-shore side-scan-sonar image strips were processed to have aspatial resolution of 0.2 m and 0.4 m in the digitalmosaics made using the individual processed stripresults. The further o¡-shore data were processedto have 0.4 m resolution for the individual imagestrips and 0.8 m in the digital mosaics. The digitalmosaics are in a UTM projection with zone 10used over the entire data set. A single digital mo-saic with a resolution of 1 m was generated usingall the data and was used for both analysis andinterpretation; this digital image mosaic productis the one available on the WWW at: http://ter-raweb.wr.usgs.gov/trs/projects/montereysonar/.

3. Digital image processing requirements

Digital data recording by any imaging systemrequires two very di¡erent types of processingstages; ¢rst is the preprocessing stage followedby the information extraction stage. The prepro-cessing stage is designed to correct an image forboth radiometric and geometric problems. Radio-metric relates to the intensity of the backscatterand the digital number assigned to a given pixel,

while geometric relates to the position assigned toa pixel. The algorithms used are customized forthe particular type of imaging system becauseeach has its own set of unique data acquisitioncharacteristics (e.g., sidescan-sonar, radar, aerialphotography, or satellite systems). In contrast,the information extraction stage uses algorithmsthat are more general and a function of the appli-cation, as well as independent of the imaging sys-tem (e.g., spatial ¢ltering or automatic classi¢ca-tion using spectral or backscatter information).The main focus in this paper is the preprocessingand digital mosaicking procedures used in thisproject.

4. Preprocessing

As with most other digital remotely sensed im-age data the preprocessing of sidescan-sonar im-ages includes three di¡erent steps: (1) geometriccorrections, (2) radiometric corrections and (3)utility/miscellaneous corrections. Keep in mindthat the preprocessing requirements are system-dependent and are often a function of the imagegeometric and radiometric characteristics. Thepreprocessing steps presented here are thoseused for the Monterey Bay sidescan-sonar images,but in general, are applicable to most sidescan-sonar image data with minor parameter adjust-ments.

4.1. Geometric corrections

Fig. 2 shows an example of the original `raw'Klein sidescan-sonar image without any correc-tions (the data were processed to increase the con-trast for visual display purposes). Some of thegeometric problems that need to be correctedare (a) the water column o¡set, (b) slant-rangeto ground-range projection, (c) aspect or anamor-phic ratio distortion, and (d) changes in the ship'svelocity.

4.1.1. Water column o¡setSidescan-sonar imaging systems start recording

data as soon as they transmit an acoustical wave,therefore a number of pixels (picture elements) on

Fig. 1. Klein sidescan-sonar ¢sh.

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both sides of nadir (center of the swath) do notcontain information about the sea£oor surface.The actual nadir pixels are o¡set to the sides asa function of the water column or ¢sh altitude.One of the ¢rst steps in the preprocessing se-quence is to merge information from the cruisenavigation ¢le into the image's navigation ¢le.The date and time of each image line are usedto extract the corresponding latitude and longi-tude and the bathymetric/¢sh altitude values atthe nadir location, from the cruise navigation¢le. At times, the ¢sh altitude values collectedduring the cruise have errors that need to be ¢xed

before processing can begin. Because of this theUSGS MIPS package includes an interactivewater column digitizing program. The altitudevalues, stored in the trailer of each line, are usedto compute and correct for the amount of o¡setin pixels. The pixel's dimensions in the across-track direction vary dramatically from near- tofar-range due to the changing view angle and cor-rections for this are also necessary.

4.1.2. Slant-rangeFor e¤ciency the program that corrects for

water column o¡set simultaneously corrects for

Fig. 2. Unprocessed sidescan-sonar image with pixels representing slant-range time, plotted outward from centerline, 0^250 ms.High backscatter is white, low backscatter is black in this and later ¢gures.

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slant-range geometry distortions. Sidescan-sonarimaging systems typically collect image data usinga near-range depression angle of approximately90³ and a far-range depression angle of approxi-mately 5^10³ (Somers et al., 1978). Fig. 3 showsthe results of applying the water column andslant-range to ground-range corrections to the im-age shown in Fig. 2. Note that the image shownin Fig. 3 has also been through the shading cor-rections discussed in 4.2.2. Speckle noise removal.The same contrast correction was applied to bothimages to make it easier to compare them with

one another. Due to the large di¡erence betweenthe near- and far-range depression angles the pix-els at near-nadir must be duplicated more thanthose at mid- and far-range in order to generatean image where pixels all represent an equal dis-tance on the ground (i.e., the same spatial resolu-tion).

4.1.3. Anamorphic/aspect ratio and ship velocityvariations

Another major geometric distortion present inmost sidescan-sonar images is the aspect or ana-morphic ratio between the along- and across-track directions. The sampling interval in thealong-track direction typically generates pixelswith a spatial resolution di¡erent from that inthe across-track direction, so a correction togive both directions the same spatial resolutionis required. The program that corrects for boththe water column and slant-range to ground-range distortions generates an across-track pixelresolution that is then matched in the along-track by the program that corrects for the ship'svelocity variations and the resolution generatedby the pulse repetition rate used by the given sys-tem.

Changes in the ship's velocity also change thespatial resolution of the image in the along-trackdirection. The ship's velocity can be in£uenced bysuch variables as the direction and strength of thecurrent and/or the wind pattern with respect tothe ship's direction of travel, and whether theship is going in a straight line or is in the processof making a long turn. Variations in the velocitycan introduce an accordion e¡ect into the geome-try of the image in the along-track direction.These types of distortions are removed by us-ing the latitude and longitude values stored inthe trailers of the image lines to calculate the dis-tance traveled over a given time frame. Giventhe distance traveled and the desired pixelsize/resolution, it is easy to compute the num-ber of pixels required for the particular timesegment. Fig. 4 shows the results of applyingboth of these corrections to the image shown inFig. 3.

If maps with acceptable resolution and geomet-ric characteristics exist for a given area, they can

Fig. 3. Image results of applying the water column, slant-range to ground-range and shading corrections.

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be used to select image-to-map control points.This allows the sidescan-sonar images to be regis-tered to a map; however, most ocean areas do nothave maps of su¤cient detail to support this typeof geometric correction. Therefore, often only au-

tomatic corrections can be made using the avail-able navigation information. Since the navigationinformation may be limited and/or have errors,the resulting corrected image will often still havesome geometric distortion. The types of correc-tions just discussed remove the major sources ofgeometric errors and are relatively easy to applyin an operational mode. Systems that collect side-scan-sonar and swath bathymetry information si-multaneously now exist and generate informationthat can be used to improve the geometric accu-racy of digital mosaics. Georeferenced bathymetrydata can be used to make pixel-by-pixel correc-tions for geometric displacements due to the topo-graphic relief.

4.2. Radiometric corrections

The second major preprocessing phase is radio-metric corrections. Geometric corrections changethe spatial position of a pixel, while radiometriccorrections change the digital number (DN) orvalue assigned to a pixel (the amplitude of thereturn signal). Typically, most algorithms dealingwith radiometry change a pixel's DN value basedon the pixel's current DN value, or as a functionof its spatial position, or as a function of both.The radiometric corrections often needed for side-scan-sonar images include (a) shading correctiondue to power drop-o¡ from near- to far-range, aswell as other lower-frequency radiometric prob-lems encountered in the across-track direction;(b) if needed, correction of speckle noise andblocky appearance due to extreme aspect ratiodi¡erences and type of resampling used; and (c)striping noise removal.

4.2.1. Shading correctionThere are several di¡erent types of shading al-

gorithms that can be applied to sidescan-sonarimages to remove some of the across-track low-er-frequency shading problems. Within the USGSMIPS we have implemented several algorithmsand currently use the combination of two in anoperational mode. They are designed to removeproblems that are dependent on either the beamangle or column location of a pixel in the geom-etry of the original raw data. The ¢rst algorithm

Fig. 4. Image results of applying anamorphic and ship veloc-ity corrections.

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currently used in the USGS MIPS processing se-quence is one in which the correction coe¤cientsare computed as a function of beam angle. Thealgorithm is similar to the one discussed by Reedand Hussong (1989). The beam angle algorithm ise¡ective at removing the near-range radiometricproblem, but is not e¡ective on problems thatoccur from mid- to far-range. The second algo-rithm is designed to remove image artifacts thatare aligned in the along-track direction, such asthe striping seen when incremental time-variedgains are used. Also, the power drop-o¡ problemis approximately along-track from mid- to far-range and this algorithm can remove most of itsundesirable e¡ects. Fig. 3 shows the results ofapplying these shading corrections to the originalKlein image shown in Fig. 2 (recall that the imageshown in this ¢gure has also been through thewater column and slant-range to ground-rangecorrections).

4.2.2. Speckle noise removalSpeckle noise is a radiometric problem similar

to that present in radar images (MacDonald andLewis, 1976). It is also similar to bit errors exceptthat the contrast di¡erence between the pixel withspeckle and the surrounding local neighborhoodis not as extreme. Pixels a¡ected by speckle do notstand out in an image as much as pixels a¡ectedby bit errors, but their spatial frequencies are ap-proximately the same. There are several methodsthat can be used to suppress the e¡ects of speckleon the visual quality of the image (this is es-pecially true when working with hardcopyphotographs which have been enlarged close tothe limits supported by the resolution of theimage). The method used on the sidescan-sonarimages in this project is one developed for radarapplications. It uses the combination of a small(3U3 or 5U5 pixels) high-pass ¢lter with an ap-propriate threshold to identify pixels with pos-sible speckle noise and then a small smoothing¢lter that a¡ects only the pixels identi¢ed as hav-ing speckle (Chavez, 1980). This procedure wascompared with several other techniques andfound to be as good, or better in many cases, asother existing methods (Chavez and Berlin,1986).

4.2.3. StripingMost of the striping noise in sidescan-sonar im-

ages occurs in the across-track or scanning direc-tion. To remove the striping from an image acombination of a high- and low-pass spatial ¢lter-ing procedure is used. The method is based ongenerating two separate images from the inputdata representing the high-frequency components,except for the noise frequency and the low-fre-quency components without the noise. The resultsof the two spatial ¢lters are then added to gener-ate an image that is very similar to the originalimage, but without the noise (Chavez, 1986). Ifthe averages of the windows used for the high-and low-pass ¢lters are represented by AVEHand AVEL, respectively, and the DN value ofthe pixel at the center of the window is repre-sented by PTXH, then as shown by Chavez,1986 (p. 1137) the following can be used to gen-erate the high- and low-frequency components(HFC and LFC, respectively) :

HFC � PTXH3AVEH

LFC � AVEL

The ¢nal results can then be generated as fol-lows:

RESULTS � HFC� LFC

In the digital sidescan-sonar image the along-track direction is represented by lines, and theacross-track direction is represented by samples.The ¢lter shapes used to remove the striping noisefrom sidescan-sonar images can vary, but typi-cally have a rectangular shape (e.g., a 1-line by71-sample high-pass ¢lter and a 9-line by 71-sam-ple low-pass ¢lter, or a 1-line by 301-sample high-pass ¢lter and a 51-line by 301-sample low-pass¢lters). This method is applicable to the removalof other noise patterns as well, the only di¡erencebeing that the shape of the spatial ¢lters must beappropriately selected for the characteristics ofthe noise pattern to be removed in the given im-age (Chavez, 1986). The appropriate ¢lters will bebased on the shape and size of the noise to beremoved.

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5. Digital mosaicking

The procedure for digitally mosaicking side-scan-sonar images is more di¤cult than thoserequired for other types of remotely sensed data,such as satellite images, because geometric con-trol is usually available only at nadir and thenavigation information often has both high- andlow-frequency errors. The general processingsequence used in the USGS MIPS package togenerate a digital mosaic from preprocessed in-dividual track-lines of sidescan-sonar image datais :

(1) Use the navigation information merged inthe trailer of each image line during the prepro-cessing steps to project the image strip into thecorrect location within the digital quadranglebeing generated on a line-by-line basis.

(2) Once the individual image lines are pro-jected based on the navigation information, whichmay include sensor pointing parameters such asyaw and pitch if available, the resulting image willusually have `holes' based on the di¡erences be-tween the yaw and pitch from line to line. Theholes are made from pixels with zero DN valuesbetween the image pixels because within the dig-ital quadrangle being generated all pixels are ini-tialized to a DN value of zero. Within the USGSMIPS we use either a procedure called LPFZ(low-pass ¢lter zeroes) or MFZ (median ¢lter ze-roes) to ¢ll in the holes. Both the LPFZ and MFZ¢lters ¢ll in the holes using the average or medianvalue of a small window (3U3 or 5U5 pixels)about a pixel ; the average or median value iscomputed using only non-zero DN values, butonly pixels with zero DN are replaced (Chavezand Berlin, 1986). Fig. 5 shows an example of aportion of an image strip geometrically projectedbefore and after the MFZ was applied. One noteto make here is that we have given the line-by-linegeometric correction software the capability toresample an image into the desired projection,but rather than always having north at the topwe can put it at any user-speci¢ed angle fromthe top. The reason for having this option isthat it makes the actual geometric correction pro-cess more e¤cient from both a computer time anddisk storage point of view.

(3) In order to allow maximum £exibility forboth geometric and radiometric matching of im-ages on adjacent track-lines two digital mosaicsare generated. The preprocessed image data areused to generate an `odd' and `even' track-linemosaic. This generates two mosaics where the im-age data do not overlap and gives us the £exibilityto work on both the geometric and radiometric¢t/matching all at once at a later stage. Once theodd and even mosaics have been made the twoare used as input to an interactive digital stencil-ing, or feathering, program. This process allowsthe user to interactively select the digital cut-linebetween adjacent strips for every pixel. By usingthis capability the user can generate what she/heconcludes is the best possible visual product usingthe `digital knife' ; however, this can be a user-intensive procedure.

Because the interactive digital stenciling proce-dure is very labor-intensive, we developed an au-tomatic seam removal procedure (ASRP) to de-crease the time required for the generation of a¢nal product to be used for interpretation andmapping. This procedure is similar to one thatwe have used on satellite image data for over 15years; the only di¡erence is the shape of the spa-tial ¢lters used. The ASRP can be very usefulbecause it allows a `good working' product tobe generated much more quickly and have aworking product in the user's hands while thedata are still relatively new. Similar to the digitalstenciling procedure the ASRP also uses the oddand even track-line mosaics as input, but insteadof interactively selecting the cut-lines to get agood tone match between images from adjacenttrack-lines it uses low-pass and high-pass spatial¢lters to help automatically match the brightnessbetween individual track-line strips. The generalidea of automatic seam removal from digital mo-saics is very similar to noise removal (e.g., stripingremoval discussed in 4. Preprocessing). The toneor brightness mismatch between adjacent strips ofimages is considered noise and a low-pass andhigh-pass spatial ¢ltering procedure can be usedto automatically remove the `seam noise'. Similarto the striping noise removal, the seam removalprocedure used on the sidescan-sonar images isidentical to that used with Landsat satellite im-

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ages, the only di¡erence is the shape and size ofthe spatial ¢lters used.

This procedure usually generates a digital mo-saic that is acceptable for analyses and interpre-tation in an e¤cient and timely manner. In manycases the results will be similar, but not as goodvisually, as the much more user-intensive interac-tive digital stenciling procedure, however, theoverall time required to generate a ¢nished prod-uct is reduced. It allows the user to generate a`good' working copy representative of what the¢nal mosaic product will look like (this could bedone during the data collection at sea). Note that

the results will have exactly the same geometricand radiometric characteristics as the mosaic gen-erated and both procedures will generate exactlythe same results over most of the non-overlappingarea which is usually equal to about 70^80% ofthe mosaic. The main di¡erence between the tworesults will have to do with the geometric match-ing in the overlapping areas. Because we are notinteractively selecting the cut-lines on a pixel-by-pixel basis the matching of spatial/geometric fea-tures will be di¡erent (i.e., the more time-consum-ing procedure allows the user to select the loca-tion of the cut-lines so they minimize the e¡ect of

Fig. 5. Image strip before (A) and after (B) the application of the MFZ procedure. Inset shows zoom of the area on the stripoutlined in the white square. Pixels now registered in geographic x-coordinates, UTM, zone 10 with north up. Swath width ap-proximately 200 m.

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geometric mismatches). However, to help mini-mize geometric mismatches of this type, we haverecently added a program to the USGS MIPSthat allows the user to identify, interactively, im-age-to-image control points on adjacent track-lineimages for use on improving the geometric matchbetween track-lines in the mosaic. Only a few im-age-to-image control points are required, there-fore it is not a user-intensive procedure. Thistype of procedure is useful when the navigationinformation does not have the accuracy requiredfor good geometric matching from strip to strip;this is a problem that is especially seen with high-resolution sidescan image data. We use this pro-gram to interactively select common image tiepoints from strips adjacent to each other and ad-just the latitude and longitude values for the lineswithin the image. The program corrects only forthe low-frequency distortions in both the along-and across-track directions in the image; the high-frequency information from line to line is pre-served, and thus the resolution of the navigationdata is not a¡ected.

6. Data distribution and visualization

In order to put a version of the ¢nished digitalimage mosaic on a WWW site and make it avail-able to others in an easy-to-access environment,new web-based software called Web Vista wasdeveloped and implemented using the 1-m resolu-tion mosaic. The software allows the interactiveviewing of the image data at either compressed orfull resolutions. Important requirements of thesoftware were a capability to view the data atboth a regional/compressed mode and/or at azoomed-in/full 1-m resolution version of the mo-saic. Options included in Web Vista are zoomcontrol for several di¡erent resolutions, roam ca-pability, latitude/longitude grid at two compressedresolutions, display image size adjustments, imagecentering, as well as blink and jump capabilities(i.e., automatically alternate between backscatterand shaded-relief images, as well as move fromone section of the image to another). The WebVista software runs on a WWW server and usesa standard web page interface. The web page ad-

dress where the digital image mosaic results canbe viewed using Web Vista is: http://terraweb.wr.usgs.gov/trs/projects/montereysonar/.

7. Summary

We have used processing and analysis capabil-ities within the USGS MIPS for sidescan-sonarimages to generate a digital mosaic of the Mon-terey Bay region using many individual strips ofsidescan-sonar data. The resulting digital mosaicis being used to help map the sea£oor in the Mon-terey Bay area. Eittreim et al. (2002) and Anima(2002), in this special issue, discuss interpretationsmade using the sidescan-sonar image data. In ad-dition, the WWW page that contains these datamakes them available to a wide range of potentialusers.

The examples shown in this paper are data col-lected using the Klein and EGpG systems, butthe software package is set up to handle and pro-cess images from any generic sidescan-sonar imag-ing system. Most of the discussion in this paperdeals with the preprocessing algorithms, which aresensor-speci¢c and aimed at correcting the imagedata for both geometric and radiometric distor-tions. The analysis and information extraction al-gorithms are very application-speci¢c and aimedat extracting and/or mapping various featureswithin the image. The resulting digital image mo-saic and products generated from the mosaicshow detailed information of the ocean £oor.The digital mosaic gives us both a large overviewpicture of the region, similar to the regional viewsof land areas given by Landsat satellite images, aswell as more local views given by the full-resolu-tion data, similar to the views given by aerialphotography. These data can help scientists mapand better understand the ocean £oor, while theprocessing techniques can help maximize the in-formation that can be extracted from the digitalsidescan-sonar images.

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