at SAR

Edge-enhan cedSe gmentatio nForSAR Ima ges

By

©Che n Ju,B.Eng.

Athe sissub mit t edto th eSchool of Gra duateStu di e s in partialfulfillment of the req uire me n tsfor

the degreeof Masterof Engineering

Fa cultyof Engineeringand AppliedScience Mem orialUniversityofNewfo u n dl and

August19 9 7

St.John's Newfo un dland Canada

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Abstract

Segmentation of Synthetic ApertureRadar(SAR) imagesis an important step for further image analysis in many applications. However,the segmentation of thiskind of image is made difficult by the presence of speckle noise,which is multiplicative rather than additive. Traditional segmentation methodsoriginally designed foreit her noise-free or White Gaussian noise corrupted images can fail when applied to SAR images.

Different methods have been previously developed for segmenting SAR images corrupted by speckle.One segmentation method was proposed by Let'! and Jurkovich whichis quite efficient;itfirstsmooths speckle noise to allow regions to bedistin- guished in the image histogram,then uses histogram thresholding to segment the filtered image. However, some problems exist with their method: in the filtered im- age,noise is preserved in edge areas and some fine regions are oversmoothed:while in the segmented image,region boundaries are ragged and some fine features are lost.

Based on Lee and Jurkevich'sinitial work, an edge-enhanced segmentation method is proposed in this thesis. The edge-enhanced segmentation method is automated and based on the iterative application of an edge-enhanced speckle smoothing filter. The edge-enhanced filters proposed in this thesis use edge information obtained bya ratio- based edge detector to improve the performance of the filters in noise smoothing as well as in edge and fine feature preservation.Due to the good performance of these edge-enhanced filters, the resulting histogram-thresholded segmented images have accurate and simple region boundaries and well separated regions of both large and small sizes. The proposed method is compared with the previous method proposed by Lee and Jurkevich,in both noise smoothing performance and in segmentation quality.

The results are tested on synthetic images as well as airborne SAR images. The tests

show that the proposed method. producesbetter imagesegmentations.particularl y inimageregion boundaries.homogeneousregionsandfor ima ges with fine futures.

The proposed edge-enhancedsegmentat ionschemema~.. be suitablefor manySAR imageanalysis applicationssuchas sea-icesegmentation.forestclass ificat ion. crop identification . etc.

Acknowledgemen t s

I would liketoexpressmy deep gratitudeandthanks tomysupervisor Dr.Cecilia

~Ioloneyforher guidance.useful discussicus.criticisms andencouragements for my work throughout the programandalsoforthe financiaIsupponshe hasprovidedme.

IwouldliketothanktheSchool of Grad ua te Stu dies.all faculty andStaffmembe rs andalsofellow gradu a testudents.

r

wouldliketoacknowledge Can adaCentrefor Remote Sensing fortheSAR imagesthey provided.FinallyI thankmyparentsfor all encoura gements andhelptgotfromthemduringthecourseof mystudy,

iii

Contents

Abst rac t

Ackn owl ed geme nt s

Tabl e ofCon tents List of Figure s List of Tables 1 Introduction

1.1 General 1.2 Motivatio n. 1.3 Problemco mplexity. lA Researchbac kground 1.5 Approach of thethesis 1.6 Organizatio nof thethes is 2 Liter aturereview

2.1 Int roducti on.

iv

iii iv viii xi

2.2 SAR speckle noise. . 2.2.1 $ARimagingsystems. 2.2.2 Sourceofspeckle noise. 2.2.3 Specklemodels 2.2.4 SARspecklereduct ion 2.3 Segmenta t ionmetho ds.

2.3.1 General

2.3.2 Segme nt at ionofSARimages 2..1 Relatedproblems.

2.•1.1 Noise smoo t hing 2.4.2 Edge detec ti on 2..1.3 Histogr amthresholdin g. . . 2.5 Concludingremar ks.

3 Th eprop osededge-en hanced segmentation method 3.1 Introduction..

3.2 Maincomponents. 3.2.1 Edge-en hancedfilters. 3.2.2 Estimationofparameter(T"

3.2.3 Edge detection 3.2.4 Numbe rof iterations . 3.2.5 Histogr am thres holding. 3.3 Overallapproach 3.-1 Concludingremarks.

10 11 11 13

23

2.

28

2'

29 30 31 3-1

3.

J<

38 38

"

4 Testresults onsy nthet ic image s 4.1 Int rodu ct ion.

4.2 Methodology 4.2.1 Test data 4.2.2 Speckle simulation 4.2.3 Performance measures 4.3 Results on synthetic image. .

4.3.1 Segmentation of clean image.

4.3.2 Histogram.

·t3.3 Filteringresults. 4.3.4 Histogram thresholding. 4.3.5 Segmented images.. 4.4 Moreresults.

4.4.1 Filtering results.

4.4.2 Use ofMSP-RoAmethod in filtering 4.4.3 ~ISP- RoAparameters 4.4.4 Modified )'fSP- RoAmet hod 4.4.5 Numbe rof iterations 4.4.6 Gaussiansmoot hing 4.5 Conclusion.

5 Testresultson SAR images 5.1 Int roduction. 5.2 SARimages . 5.3 ResultsonSAR images .

vi

42 42 43 43 44 40

4 ,

4, 4,

48 55 55

5 ,

59 62

64 65 67 70 70

71 71 71 72

5.3.1 Hist ogram 5.3.2 Filterin gresul ts . 5.3.3 Histogramthresho lding. 5.3.4 Segmented images. 5...1 Overalldiscussion.

6 Concl us ionsand re com mendat ion s Refer en ces

vii

72

90

93

List of Figures

2.1 Atypicalmulti-lookairborne SARimageFit.ld.5..

2.2 Histogr am of tbe SAR imageFi~ldJof Figur e 2.1.

2.3 MSP-RoAscheme_.

15 26

3.1 Using edge inform at iontodefine thetlGl1d regioninthe filteringwindow 33 3.2 Flow diagramofo-...erallaproacb..

4.1 Original ima ge Combin e

4.2 Specklecorruptedimages:(a )ComJrineCDRl.I-look:(b)ComJnneCOR.

-I-Iook.• .

as

51

'"

,.

4.3 Segmentation ofclean image Combine -1..1 Histogr ams of(a)Com bine(b)Comb'in e COR

-1.5 Filt eringresu lts forCom blneC OR.mask11x II.3ite rations: la) Using iter a t ive Leemultiplicati vefilter:(h)Usingedge-enhancedLet' mult iplicat ivefilte r..

4.6 ~fSP-RoAedgemapofCombm eCOR<a) Beforefilte ring; (b) Before thethirditerationof the edge-enhancedLee multiplicativefilter. 52 4.7 Edge-enhanced mean and median filtering resultfor CombineCOR 53

viii

-1.8 Histogramsof filtered image of Comb'ine COR:(a) After iterativeLee multiplicativefilter:(b)After edge-enhancedLeemulti pHcat i\-e filter:

(c)Ga ussiansmoot hingof(a); (d)Gaussiansmoot hing of(b). 56 -1.9 Segmentation results onComhineCO R:Ca)Basedoniterativelee mul-

tiplkativefilter;(b)Based onedge-enhancedLeemult iplicativefilter 58 4.10Comparisonof filtersappliedto ComblneCOR:[a ]Bestsmoothingby

iterath-eLee filte r.5)( 5window.-Iitera tions:[b]Bestsmoothingby edge-enhan cedLeefilter.11x11window,3itera tions.

-1.11Demonstr ation ofusing themodified MSP-RoA:(a)Edge mapbefore filtering;(b)Edge-enhancedfilteredresult.window size IIx11.3

63

iterat ions. 66

-1.12 Moreiterati ons:Ca) Filtered image; (b) Segmentedimage. 68

-1.13Histogramof Figure 4.12(a). 69

5.1 \tult i-Iookairborn e SARimage:(a)Fields;(b)Industry ;3

5.2 Histograms:(alField.r.(b)/ndw try. 7-1

5.3 IterativeLee multiplicative filtered result of(a)Fiel ds:(b)/ndTJj try . 76 5.-1),lSp·RoAedgemapsofFields. (a)Befo refiltering;{b}Before the 6nal

iterationof edge-enhancedlee multiplicativefilter.

5.5 MSp·Ro Aedge maps of Industry:(a)Beforefilterin g: (b)Before the 6nal itera tionof edge-enhancedLeemultip licativefilter.. 78 5.6 Edge-enhancedLeefilteredresultof(a)Fields:(b)Indu.4try. 79 5.7 Hist ogramsof filtered images:(a)Field.!after itera tiveLee filtering;

(b)/ndwtryafter iterativeLeefiltering:(elFieldsafteredge-enhanced Lee 6ltering;(d)/ndwtryafter edge-enhancedLeefilte ring. 82

ix

5.8 Gaussiansmoot hedhistogramsoffilteredimages: (a) Fields after Lee filte ring;(b) IndustryafterLeefiltering;(c)Fieldsafteredge-enhanced filte ring;(d) Indu.stryafteredge-enhancedfiltering. 83 5.9 Segmenta tion result s of Fields: (a)Based on iterat iveLee filter; (b)

Based onedge-e nhancedLee filte r. 85

5.10 Segmentationresultsof Industry: (a)Based on iterativeLee filter; (b)

Based on edge-enhanced Lee filter. 86

List of Tables

4.1 Quantitativemeasures of itera tiveLeemultipli ca tivefilter 60 4.2 Quantitativemeasures ofecge-eehancedLeemultiplicativefilter. 60 4.3 Edge-eebaaced filtering perform an ce when using ),{Sp ·RoA once or

usingit iterat ivelyineach applicationofthe filter . .

.{..I Performanceoftheedge-enhancedLeefilterwhenchoosingdifferent

parametersforMSP·RoAedge det ect or.

.4

65 .1.5 Performance offilter withoriginal and modifiedMSP·RoAedge detecto r6;

5.1 QuantitativemeasuresofiterativeLeemult iplicat ivefilterappliedon realSARimages

5.2 Quantitativemeasuresofedge-e nhan ced Leemulti plicat ive filterap- pliedonreal5ARimages.

xi

88

Chapter 1 Introduction

1.1 G en eral

Segmenta tio n isoneof themajortasks indigitalimageprocessin gandanalysis.

Thepurpose ofsegmenta tionisto divideanimagetate regions which are uniform and bomogeneouswith respecttosomecharacte risticssuchasgraylevel or texture.

Segmentationcanbecritical forsubsequentanalysisand scenedescrip tion.

SyntheticAperture Radar (SARlut ilizes a~'lltbeticapert ure togenera tehigh resolut ionimages of terrainfromaconstantemissionof microwavepulses.The syn- thesizedapertureisgenerated bythemot ionof therad arpla tform.which canbe eit heranaircraftor asatellite.andbysignalprocessingoftheret urnedpulses.SARs have several featureswhich makethem valuableforremotesensing:theycancol- leerimagesbyday and at night.andinalltypesofweat her;andthe microwave regionoftheEM spectrumprovi des unique informationabout the terrainsensed.

Because ofthese features, SARhasbecome an euective technologyfor monito ring manygeopbysicalparamet ers.ApplicationsofSARimage processingincludeland

coverclassifica tion.soil moistur e measure ment, foresttyp eclassification .measur e- ment of liquidwater conte ntof vegetation. snowmapping, sea ice type classification, ice sheetdynam ics. oceanography',andmany others

'. HJ.

1.2 Motivation

Thegoalof segment ingSAR imagesis to produceimageregions each represent ing ground regions suchaswoods,fields,roads, etc. Thisis essentia l forapplica tions such as crop identification,terrainmappi ng, target detection.etc.Segme ntationof SAR imagesisusuall ybased ongraylevel or texture. Gray levelbasedsegment at ionis useful for theidentification of features [48] and fordetecting changes between differen t images [9].Texture measuresare helpfu l.for exampl e,inseaicedetec tion [46]. In thisresear ch,wefocus onsegmentat ion basedonsim ilargray level, aswillbe furt her explained.

Segment ing aSARimageis made difficult by thepresence ofspeckle noise which is multiplicative in thesensethatthe noiselevel increaseswith themagnit udeofrad ar backscanering. There areseveralmethods thathave been developedto segment SAR images. However , someproblemsstill exist,including the accuracyand simplicity ofregion bound aries,homogeneityofregions.ease ofuse and imp leme ntationof segmentation method s, computa tio na lcomplexityof segmenta tion algorithms,etc.

The goalof this thesisistoprovideautomatic,unsup ervi sed segmentat ionschemes for SAR images whichare efficient and accuratein dividingimageregions,and to compare thesenew schemes withthework of past researche rstodemonst ratethe advantagesof the proposed schemes.

1.3 Problem complexity

SegmentingSARimag es is generallyadifficu lttas k.The complexit)·Involvedinthe above mentio nedproblemarisesfromthree majorfactors. First of all. thesignals tobe processed are two dimensio na.lsignalswhichare randominnature.sotheir statistical propertiesarenoteasyto est imate.Second . thereis nogeneraltechnique and complete theory forsegmenta tion.Image segmentationtechniquesarebAsicall)- ad hoc anddifferpreciselyinthe"raytheyem phasizeDo eorma n!'desiredprope rties andinthe waythey balance andcompro mise onedesired property againstanother.

Third, thepresence ofspeckle noisemakesthesegmenta tionofSARimages more difficult.This kindof noise ismult iplica tive andnon-ad ditive.unlikemanyoptical imageswhichmayhave additive whiteGaussian noise.~Ianytraditionaltechniques for segmentationwhichwereoriginallydesigned foropticalimages rely onmeasu res based ondifferencesbetweenpixelintensities;hence.such met hods suffer from noise artifactswbenapplied to SARimages[4].For example.tradi tionalclustering tech- niques forimagesegm enta tiontendtoform more clust ers in brightareas thaninfaint ones[441.

1.4 R es earch ba ck ground

There isnogeneraltechniqueforsegm entation.becauseof thedifferencesin appli- ca tionsandimage type s.Many segment at ion schemeshave beendeveloped.based on diffen!ntcriteria andfor use with differenttypesofimag es.Image segmentation techniquescan beclassified as:graylevelthresholdin g,iterativepixelclassification , methods based on fuzzyset theory,etcf40].Somespeckle-s pec ificmet hod s havebeen developed forsegmenting SARimages.Tbese usevarioustechniquessuchasstmu-

lated annealing[25], wavelet transforms[3].hiera rchical rando mfield models[13].

neural networks[22), fuzzy c-means clustering

1 1-11.

etc.

A method for segmentingSAR images was proposed by Lee and Jurkevich[31], who foundthat the iterative applicationof a specklereducing filter(based on theLet>

multiplicative filt er[32])produ ces an image witha mult imoda lgray lew·1histogram suitab lefor thresho ld ing. Thismeth odis efficient,relativelyeasy to use and unsuper- vised afterthe sett ing of initial parameters.Itproducesgood results and mayoffer significa ntadva ntages over tech niquesbased on region growing with its abilityto seg- mentclasses separatedby a grad ua lcha nge ingray le vel intens ity

1 471.

However.it has severaldisadvantages. First of all, althoug hthe filteringschemeisunsupervised.

it requires the knowledgeofthe ratio of standarddeviation to mean in homogeneous imageregions.InLeeand Jurkevich'simplem ent a tion.thisinpu t parameter remains unchan ged .thoughits actualvalue may changeoneachit erati on . Second,theLee multiplica ti vefiltertendstopreserv e speckle noiseinedge areas .Third ,theirmeth od requiresthat usersmanuallydeterminethevalleysill the histogramof thesmoot hed imagefor thresho lding purp oses. To overcorne thesedisadvantagesbut stillret ain the advant ages ofLeeand Jurkevich'smet hod . in this thes is we proposeand test an edge-enhancedsegmentation methodwhichis based on the initial workby Lee and Jurkevich.

1.5 Approach of the thesis

Theedge-enhancedsegmentationscheme proposedin thisthesisisbased onthe re- peated applicationof an edge-enhancedfilter. My approach to the solut ion of the statedsegment a t ion problem involves thefollowing steps:

•Lsinga ratio-basededge detectortogeneratean edge mapof a SARimage:

•Cboosiog a specklereducingfiltersuitable for iterative applica tion:

•Modifyingthefilte rby usingedgeinformat ioninlocal statisticalanalysis{Ica ll this modified filter an edge-enhanced filter):

•Applyingthe edge-enhanced filteriteratively:

•Segmentingthe filtered imagebasedOD.histogram thresholdin g.

Theperforman ce of the edge-enhanced segmentation methodistested on both syntheticand realmult i-look airborne SARimages usingseveraledge-enhanced filters. Compariso ns ofthe proposed method withthe previous methodofLeeand Jurkevich are alsoprovided.The testresultsshow that theedge-enhancedmethodcan separate the homogeneous regionsvery welland thattheregion boundariesare simpleand accurat e.Itprovidesan automatic and efficient segmentation methodforSARimages.

1.6 Organization of t he thesis

Thisthesishas been organizedas follows.Chapte rTwo provides background informa- tion ontheareas relatedtothe statedproblem. ChapterThree provides descriptions of my proposedsegmentationmethod.Test results andcomparisons are presented in Chapter Fourand Five.onsynt hetic andreal SARdata.respectively.Finally,con- elusionsbased ontheresultsobtained andsuggestionsfor furtherwork are provided in Chapter Six.

Chapter 2

Literature review

2.1 Introduction

Thischapterisgrouped intolive major sections. Thesecond section provide sa briefintrod uctiontoSynthe t icAperture Radar(SAR) imagingsystems.SAR speckle.

speckle noise modeli ngandSARspeckle redu cin gtechniques.The thirdsect ionis anoverview of different existin g segmenta tionschemes.Speci al attentionispaidto LeeandJurkevich'smethod(31] and Smith'swork [HI_Thenext section discusses the problems rela ted to SAR imag e segmenta tion,particularlynoise smoo t hing.edge det ectionand histogram threshold ing. Concludingremarks areprovidedinsection Five.

2.2 SAR speckle noise

This section will int rod uceSARimaging systems,the source of speckle noise.Also.

itwill providea briefsurveyof previousworkonSARspeckle modellingandspeckle

reduction techniques.

2.2.1 SAR imagingsystems

A SARisaremote sensingsystemusedtoobtainhighresolution.twodimensiona1.

microwave imag es of targeted terrain.BeforerbedevelopmentofSARs. itwasvery difficultto acquire this type ofimage£rommicrowave syst emsduetorhe extremely largeantennasneededto obtain thehigh spatial resolution.SARtechnologyavoi ds thisproblembyutilizing the motionof the radarplatformto synth esizealarger ante nna.Theplatform fora SARcan beeitherspace bom eorairborne.Asit moves over an area of interest,it constantlysendsmicrowave pulsestowardsthegroundat discrete intervals.TheseIntervals are quiteshort.and each pointon tbegroundis mapped numero us times by different microwavepulses asthe radar platformmoves past.Thisrepeated expos ureofa groundpointtoman)'differentpulsesgivesthe impression tothe imaging systemthatthe lengthofthe antenna is nolongl"rJUSt its phys ical size.butrat her.the flightlengt hoftheradar plat formduringwhichagiven pointiswithintheradar's swat h.After severalsta ges of proc essing suchas quadratu re demodula tion.rangecompression.azimuthcorrelationand focus corrections.SAR imagescanbeobt ainedfromcollected. digitizedSAR da ta[11.

531 .

^{Figure 2.1shows}

anairborne SAR imageoffieldsand trees.

2.2.2 Sourceofsp e ck le noise

InSAR and othersyste msem ployingcoherent illum inationtoform high resolutio n images. theresul tingimageisgenerallycorrupted byaformofmultiplicativenoise known ascoherentspeckle.This severeform ofDa ise.characterized byalowsigna lto

Figure 2.1: A typicalmuhi-lookairbo rneSAR imageField.q

noise ratio,presentsproblemsfor image processingsoftwart'of all kinds.Inpa rticula r . manyimage analysistechniquesoriginallydesignedforopticalilllagl~sufferfrom1l0iSI'

artifac tsandperfor m poorlyWh Cl 1applied to SARimages[..I.4-1.491.

Speckleis theprimarysourceof radiometri cdistortionin SAR Images and arises from threeprimary sources:first,a resolution c{'11 is many wavelength s long at tln- frequency of theSAR:second.the c('11 typically contains not one but manyoh-meutarv point scatterersat differentorientationsann havingdifferent areas andscatu-nngeo- eft iciems: and third.the resolutioncell isnotviewedinstantaneouslyhUIover ashort intervaland consequently theviewingangleand scattering coeffk ients chang!'during imaging.Toform an imagepixel. theco mple x point spreadfunctionsr-omribured hy theset ofsca n erers in th('resolut ion cellan' summed cohereut .lywiththediffer- eut phases inter feringconstructivelyor destructively, yielding a speckle uoiv pattern

superimposedon theimageof interest.

2.2. 3 Speckl e mod el s

To designan opti mumtechnique for SAR image analysis,itisnecessaryto haw an appropriate mat hematicalmodel of thespeckle noise ba....ed on themet hod of image format ionand its statisticalpropert ies.Specklepropertiesarediscussedin several papers[I,2, 23J.It is wellestablishedthatspecklenoiseintensity is proportionalto the underlyingimageintensity , givinga Signal toNoiseRatio (S:\ R) inan observed speckled imageequal to onefor fully developedspeckle. Thisimplies thata poi nt wise multiplicativemodel would be ableto describe speckle inSAR inte nsityimagesquite well.Alarge amount ofresea rch is found which assumesamultiplicative modelof speckle[17, 30,32]. The modelcan be representedin all(i,j)spatialcoo rdin a te systema.",

,(i.j)

=

x(i,j) . v(i,j) (2. 1)

wherexistheoriginalimage.zisthe recorded image anduistheran dom noise process.

However,ithas beenpointedout byTuretoJ. 152]thatamult iplicat ivenoise model is notalways complete.The main disadv ant age ofthis model is thatit does not take into accoun tthe correlat ionofspeckle whichis an importan t conside rat ion insomecases. The correlatio ncan beseen to arisemainlyfrom thePointSpread Function (PSF) ofthe imaging syst em . Thusa bettermodelcanbeobtain edby takingthePSF of the imaging system into account. Thus,themodelofEquation2.1 callbe re-writtenas:

,(i,j)=(x(i,j) . v(i,j))•h(i,j) (2.2)

where.indicates spatialconvolut ion and h isthe SAR impulse response.Themod el describedby Equation2.2 isusedb~.. Frost erai.with theassu m pt ionthat the PSF forSARsyste ms is an impulse.Inaddition .more complex model s ofSARimagecall beformulated,to account forpointscane rers.linefeatures. etc[38].

Leeand Jurkevich

[311

showed how the mean and varianceof thenoiseproces s var ied for one-look and multi-lookSAR imagemod els.Thevariationduetointen sity and amplitude images is alsoaddressed in their paper.

2.2.4 SA R spe ck le reduction

Someimage processing methods ha vebeen proposedwhichexplicitlyaddressthe mult iplicat ive natur e of the speckle noise. Thesemethods includ e theuse ofspeckle reducing filters, theloga rit hmtransform and techniquesbased onra t ios of pixelin- tensities.

Severalspecklesmoothing filters have been proposedbasedon a statisticalmod el ofspec kle noise.These are theLee multiplicativefilter 1321 and itsva riat ions,e.g [36],theSigma filter[33. 3-1],theWeightedfilter(39]. Li'smethod (37], theMaximum APosterior(~[AP)filter138], etc.

One waytodeal with the scaling of the variancewith imageintensity istowork in the logarithmic domain.Under thelogarithmic transformation,the noise variance becomesindependentof theimageintensity[4],althoughthe logarithm changesthe st a t ist ical and frequencydistributions of the noise process.

Another way towork withthe multiplica t iveaspect of thespecklenoiseis using similar itymeasuresbased onthe ratio of avera gepixelint ensit ies

[18,

49].Ithas been shownthatthe rat io ofaveragepixelint ensiti es wit hina homogeneo usareafollows theFdistributionand is inde pende ntof the image intensit y

[41.

10

2.3 Segmentation m ethods

Oneof themostwidelyusedstepsinthe processofreducing images to informationis segmentat ion; that is.dividing theim a ge intoregions tha tco rrespond to struct ured units in thescene orwhichdistinguis hobjectsofinterest.Animage canbesegmented intohomogeneousregionsbasedODsomefeat uressuch asgray level or texture.This isimportant forsu bsequent analysisandscene description [43.-151. Then'af t'many types of images.suchasopticalima ges.magneticresonan ceimages(:\IRll.range images.infrared images.SARimages.etc.Hundreds of segmentation tech niques are presentedintbeliterat ure.but thereisno singlemethodwhichcanbe considered good forallimages.norareallmethodsequallyr;ood(or aparticulartyp e of image [401.:\{oreover.algorithms developedforODeclass of imagemay not performwell when appliedto ot her classes ofimeges.Thisisparticu larlytru ewhenthe algorith m is basedona specific image formationmodel.

2.3 .1 General

Agood imagesegmentationrequiresthat regions of an image segmentatio nbeuniform andhomogeneous.region interiorsbesimpleand without many smallholes.adjacent regions of asegmentationhave significan tly differentvalues,andth atboundar ies of each segmentbesimp le.not ragged.and spatiallyaccurate12-11.

Achieving all thesedesired propert iesisdifficult because strictlyuniform and homogeneousregio ns are typicallyfullofsmall holesandhaveraggedboundaries.

Insistingthatadjac ent regionshave largedifferences in values can cause regions to mergeand bound aries to be lost.

Ima ge segmentationtechniquesare basicallyad hocanddiffer preciselyin the

11

way they emphasizeoneor moredesired pro perti esandin the way theybalance and compromise onedesiredpropertyagainstanother.

Image segmenta tio ntechn iquescan be classifiedas gray level thresholding.irera - tivppixelclassification.meth ods based onfuzzy settheo ry. etc.(olD].

Thresholdingisoneof the oldest,simple and most populartechniquesforimage segmentation.Thresholdin g ca nbe performed basedongloba lInform a tio n[e.g.gray levelhistogram of the ent ice imag e)or using local information(e.g. co-occurence matrix)ofthe image.Ifonlyonethresholdis usedfor theentireimage.then il iscalled globalthresholding. On the ot herhand.when the image is panition ed into severalsubregions and a threshold isdetermi nedforeachof the subregions.it isreferredtoas local thresholding.Typically,thresholdingmethodsworkwellin situa tio ns where there are a few distin ct objectshaving widelydifferi ng gra:y tone intensit ies andthese objects appear on a near uniform.background[2-11.

Iterativepixel classificatio nmethodsinclude region grcwiag,relaxation.\larko\·

RandomField (MRF) basedapproachesand neural networkbased ap proaches.Re- gion growing schemesregardsomeofthe pixelsinthe imageas nodes andgrow regions basedonsome simil ari tycritera.Relaxationisaniter ativeappr oachtosegmentation inwhich the classifica tiondecisions abouteachpixelcan be takeninparallel143].

Therearemanyimage segmentationmethods whichuse spatialinte racti onmodels suchas Mar kov RandomFields (MR F)or GibbsRandomFields (G Rr)tomodel digital images(12,20].Severalauthors have atte mptedto segment an im age using neural networks(7,211.

Fuzzysettheol')·isusedin fuzzy thresholding,funy clustering.etc. Different histogr am thresholding techniqueswhichminim ize the graylevelambiguityandgeo- metricalambigui tyofanim age aredescribed in141,42].The fuzzyc-mean(FC M)

12

clustering algorithm[6]has been usedinim age segmentation[511.

2.3.2 Segment ation of SA Rimages

WithSAR images ofmixed terrain.thegoal ofsegment ation might beto produce imageregions eachreprese nti nggroundobjects such as woods. fields.ve getation type . roads.urban areas.etc.This is essentialforapplicationssuch as cropident ification . terrainmappingandtarget detect ion. etc. Prod ucing thesegment ati on ofa SAR imag e ismade difficul tbythe presenceofspeckle noise.Severalsegment at ionmet hods specificallydesigned forSARimages havebeenproposed based oneit hergray leve l or text ur e.

Hegarat-Mascle etal. [25)proposed an algorithm forsegment ingSARimages whichappliessim ulated annealing techniq uesinthe classification process.Benieetal.

[5] developed a classificationmethodwhichintegrates two algorithms:a hierarchical

imagesegmentat ionbyste p-wise optimizationto takeintoaccountthe spatial context.

andan iterati vecondi ti onal mode(reM)algori thm to classify thesegmentedimage.

Barbarossa etaI. [3] proposed a method forclass ifying SAR images basedona multiresoluti onrepresen ta t ion ofthe imagesobtained byawavelettransfo rm.Derin etat. (13] describ ed some algorithms based onahierarchicalrandom field mod el pro posed for speckle images and a~laximumAPosterio r(MAP)segmentation using simulatedannealin g.Fosgate etaL.(15]present ed an efficientmultiscaleapproach to thesegme ntationof naturalclust ers . Grossbe retal. [22Jdevelopeda neural network model of boundarysegmentation and surface representation toprocess images containing ran ge datawhichisgatheredbya SAR sensor.Du et01.(14J used a fuzzy c-meansclustering methodforunsupervisedsegmenta tionofmult i-look polarimet ric SARimages. Texturealsoisimportantin characterizin gthe inform ationin SAR

13

images. Sometexturebasedsegmentationschemesare proposed in theliterature.

Ceccarellietal.

[101

use texture informationandneural networks forSARimage segmentation.Sephton eral.[48Jproposed a segmentationalgorithmfor SAR images ofsea-ice based onvariousatt ib ut essuchastextur e,shape,posit ion.etc.

One segmenta t ion methodfor SAR imageswasproposedbyLeeandJur kevich

[311,which is simpleand efficient.Itisunsup ervised and produ ceswrygood results.

It mayoffersignificant advanta gesovertechniquesbased onregion growing with itsability to segmentclassessepara ted b)' agrad ualchangeingrayle velintensity

[471.

Smit h

[471

extended thismeth od and presentedafull)'automaticsegmenta tion schemeforsegment ing SARimages.Based on thispreviouswork ,Iproposetheedge- enhancedsegmentation method in thisthesis.Thus.it isimportant tohave amore detailed lookat theworkofLeeand Jurkevich aswellasSmith'swork.

Leean d Jurkev ich 'smethod

Image segmenta tion is often based onedge detectionand region growing[9.-I8J.This approachisdifficult to applyto images corrupted byspecklebecausegapsinedges, caused by speckle,mustberepair edwith abondingroutin e 148J.Another problemis that ill-definededges,formedb)'a grad ua lchangein intensity between twoclasses,

\\;11notbedetected.Loweringthe edgedetection thresholdin ordertodetectfewer sharpedges often resultsin thedetecti on offalse edges,lead ing to over-segmentation ofthe image [9].

Anatura lapproachto unsupervisedsegmentation based on gray levelsisto select thresholdsatthe valleysof agray level histogram.ifsuch valleysexists.For SAR images.however,the speckle appea ring in them comp licatesthe characteristicsofthe SARimagehist ogra m and makesautoma tic segmentationofsuchimagesdifficult.

l~

e

Figure2.2:Histogram of theSARimage Fields of Figure2.1.

The histogramin a SAR image is typically unimodal 131]and difficult for histogram thresholding. Furthermore,since no use is made of spatial information insimple histogram thresholding.there is nogua ran tee that pixels of one gray scaleclass will be contiguous. Figure 2.2 shows the histogram of the typical SAR imageFields

whichis shownin Figure2.1;alt houghthisimage is a relatively simp leSAR image, its histogramdoesnot reveal distinct image regions.

A method for segmenting SAR images corruptedby speckle' was proposed by Lee and Jurkevich[311, who found that therepea ted application of an edge-preserving speckle smoothing filter produces an image with a multimodal gray lewl histogram suitable for thres holding,even if the histogram of the originalimage is unimodal.

Thera tionalebehind thisapproachis that theaveraging processwill redu ce thenoise standar ddeviat ion,thustending to producesharp peaksin the histogram, while the edge preserving effect of thefilter produces deeper valleys.

Of crucial importance to this techniqueis the ability of the speckle filter to smooth out the noise without destroying edges and fine features.Leeand Jurkevich iteratively

15

used the well-known Lee multiplicative filter

[32J(reviewed below in Section

2.4) in

their segmentation

process. This filter is quite effect ive ill removing speckle

especially

in homogeneous or low variance areas. In high variance areas, however, the filter's parameters are adjusted to preserveedges; this has the effect of also preserving

speckle

noise near and on edges.

Overall

,

Lee and

Jurkevich 's

method is quite simple and

efficient. This

unsuper - vised algorith m will segment the image into several classes without a priori knowledge of

the number of

classes and their mean values. However, there are some disadvan- tages to this method. The input parameter

(t he ratio of the standard deviation to

mean) to thefiltering algorithm should be calculated manually and in their

implemen-

tation this parameter remains uncha nged, though the physical quantity it describes

(i.e. the speckle noise)

may change on each iteration.

In

thresho lding, the

histogra m

valleys are user-determined . Moreover, due to the poor performa nce of the filtering algorithm

in edge areas,

the segmented image regions have ragged and inaccurate region boundar ies.

Smith

's

work

Smith improved Lee and Jurkevich's method and proposed a fully automatic,

unsu-

perv

ised

segmentation algorithm, based on the iterative application of the modified sigma filter

1

471 (also

reviewed below

in Section 2.4).

One contribution of Smith's work is that he proposed a method to automatially estimate the input

pa rameter,

the ratio of the

standard deviation

to mean

,

in the filtering algorit

hm.

For estimation of this parameter , the entire image is partit ioned into equal sized windows [e.g. 3 x 3) and the ratio of standard

deviati on to mean

calculated in each window. Then, the

histogra m of these local estimates is obtained,

16

and the histogram mode is taken as an overall estimate.

Also in Smith'swork,a modified sigma filter has been proposed which is shown tobe an improvement over the basic sigma filter inpreservingedgeswhilesmoothing the speckle noise.Moreover, the histogram thresholding procedure isfully automatic.

However,Smith'smethod is not ableto detect small classes whosegra~·level falls withinthetwo-sigma range of larger classes.

2.4 R elated problems

~Iyresearch isbasedon the segmentation method proposed by Lee and Jurkevich. As stated above,the most important component in Lee'smethod is the speckle reducing filter.Soit is desirableto have a review of SAR noise smoothing techniques and filters suitable foritera ti ve application. Because 1alsouse anedge detector in my proposed segmentationscheme toimprove theboundar y accuracy,SAR edgedetecti onmet hods willalsobe reviewed. In addit ion, the hist ogram thresholdingmethodthatis usedin my researchwillbedescribed in thissection.

2.4.1 Noise smoothing

Noise canbereduced by filtering, in which a moving window ispassedover each pixel in the image,and the pixel value is replaced by a value derived from the pixels within the window. Nume rousspeckle reducingfiltershave beenproposed and discussed (see,forexample[35, 54J). Here, we describesomespat ialfilte ringmethods that use local statistics and whichmaybe suita blefor iterativeapplication.

17

Mean and medianfilter

Twosimplefiltersare themea n andmedian filters ,ill whichthe centra lpixelis replaced by the mean ormedia n,resp ectivel y,of all thepixels illthewindow.The mainproblemwit hthe mean filter is that theedgesintheimage aresmoothed toget her with the speckle noise,and maybelost completely after a few successive applicationsof the filter. The medianfilter will preservestep edgesin noise-free images,however,inspecklecorru ptedimages.itdoesnotremovethe noisevery well.

sincespeckle noiseisnot impulsive.

Leemu ltiplicativefilter

The Lee multiplicative filter132]isbasedon a multiplicativenoiseimagemodel:

'(i,j)

=

x(i, j ) . v(i,j) (2.3)

wheree.xandvdenote the observed image,underlying image andnoise processes.

respectively. Basedon anassumption that the noise iswhitewith unitymeanand isuncorrelatedwith the imagex,theLeemultiplica tivefilter seeksthe bestmea n- squaredest imat ef of x,At each pixel(i,j),

i(i ,j )=i(i,j)

+

k(i ,j )(, (i ,j ) - i(i,j))

wherethe ga in fact ork(i,j )canbeobtained as

k(i , j )

= _ . . 2V~r~(i,j)

. . X(l, ) ) at!

+

Varz(l,) )

(2.4)

(2.5)

where0"isthe ratio of standard deviationtomean in homogeneousregions.Theloca l adaptation of tbe filteris based on the ca lcula tionof thelocalsta t ist ics,

x

andVar~ ,

18

fromthe datasampleestimates!and

ver,

determi nedover a localneighb ourhood window:

t(i ,j)

=

!(i.j) Var ,,(i,j)=Var,( i, ;)+z(i,j )2_Z(i,j)2

(7..+1

12.6 )

(2.,) By adapti ngits parametersto both low-variance areas and higb-vanance areas.

thefilter bothsmoothsnoiseand preserves edges. In orderto preserveedgt'5.the filter essential lyshuts itself off inhigh varian ce areas[i.e.k(i,j )::::;I)sothatthe estimatei(t , j )is approximatel y equal to theobse rved pixelvaluez(i,j).Thismeans thespecklenoise is preservedinhigh cont rastregions.

Leeand Jurkevich(311found several itera tionsof theLeemultiplicativefiltercan greatly red ucespecklenoise.However,small detailsmay belostdue tothe repetitive smoothingoperati on;and,aswith theone-passLeemulti plicativefilte r.speckle noise is preserved inedge areas.

RefinedLee multiplicat ive filter

To improve the performance in edge areas,Leeproposed a refinemen ttothe original Leemult iplicative filt er[361.inwhich the neighbou rhoodused inhigh varianceareas forthecalc ulationofthelocalstatistics takesinto account the orient a t ionofapossible edge.Foreachpixelwithlocal variance Var,exceed ing a setthres hold.oriented gradients are comput edandusedtoselecta subsetofthe neighbou rhoodpixels on oneside oftheedge and most like thecentralpixel.

ver,

estimated over this subsetwill ingeneral be lowerthanthesample varianceover the wholeneigh bo urh ood,allowing more accurate filtering of noise. However.theedge detectionisnot optimized for specklecorru pt ed imagesin which localvarianceisrelat ed not only toedgesbut also to theunderlyingmeanintensit ylevel.

19

Sigmafilt e r

Lee 133J proposed a much simpler alternativetotheLet'mulriplicarive filter, based on the same multiplicative specklenoise model.Itiswellknowthat 95.5 percentof normallydistributed random samples fall within two standa rd deviations011eit her side of themean value.Valuesoutside of the two-sigmarange arethereforelikely to befrom a differentdistribution,The sigma filteraverageson ly those pixelsinthe filter window which lie within thetwo-sigmarange of thecentral pixel value.Edges arethuspreser ved becausepixelsnot belongingto thesame dist ribu tionas thecentral pixel areexcludedfrom the averagingprocess. Sincethe speckleismulti plica ti ve,the two-sigmara nge, as sumin g thecent ral pixelz(i,j)to bethemea n of itsdistr ibu tion, ishoun dedbelow andaboveby

'm,.

^{=(1 -}2a.)'(i,j ) ...~(1+2a. ), (i, j )

(2.8)

(2.9)

Thusonlypixelsz(k, l )whosevalue lies bet ....-een z.",nandZmcu are included in the ca lcula ting the estimateofi(i,j).

A problem arises if there areno ot her windowpixelswithin the two-sigm arange, Such sharp spot noiseis dealt with by introducing a thresholdk,such that, if the totalnumberof pixels within thetwo-sigmarange is smallerthanor equa ltok, then thecentral pixelisreplacedb)' theaverageofits fournear estneighbour s,

This filterisbased onthe assumpt ionthatspeckle noise has a Gauss iandistr ibu- tion;therefore ,itca n beapplied to the dataprocessedinala rgenumber oflooks.

Whenthiscond ition isnot satisfied,the filt eredimagehas anasymmet ricdistr ibutio n [39J, Furt hermore,thisfilterdegradespoint targets because it replacesthecentral

20

pixelvaluewiththe mean ofits fournearestneigh bourswheneverthe central pixel has an extremevalue.

Weightedfilter

The sigma filterasdescribed aboveisbasedon the assumptionthatthecentr al pixel isin factthe mean ofits Gaussiandistributio n.Amore general perspective.proposed byMarrin andTurner[39],isthatpote ntially the centralpixelbelongs to ara nge of Gaussian distri butions.For maximum speckle reductionit is desirabletoinclude in the averagingproc essallpixelswhich could possiblybelong to thesame distribution as thecent ra!pixel.whileexclu ding those pixelsclear lyfrom differentdistributio ns.

Furthermore.itiscrucial forthe preservationof fine featW"tS.llihichby definition contain fewpixels within thefilter window, thatpixelspossibly belonging to the samedistribution as the centr al pixelare notexcluded fromtheaveragingprocess.

Foreach window pixel.l(t,i)itmust thereforebe determined whether.::(t.l)and .::(i.j )are both withi nthe two-sigmarange ofthe same Gaussian distribution.Ifso.

thenz(k.l)shou ldbe includedin the average.

Martin andTurner1391 proposed a newsigma filter .which theycalled tbe weighted filter.suchtbata window pixelz(k .l)is includedin tbe averaging process ifaGaussia n dist ribution cent redonz(k,l)with standard deviati ont7v·:(k.l)hasthecentral pixel valuez(i,j )withinits two-sigma range.Theweighted filtertherefore averages all window pixels lying between... and

z.-.:

givenby

z(i,j)

z...

⁰⁼1+2t7"

z(i,j) Zmll:f=~

21

(2. 10) (2.11)

Theweighted filte rassignsweightstoeach pixelbasedon the Gauss ian proba bilit y densnyfunction cent redonthe pixel value. Thisisacomp uta tional ly expensive procedure.requiri ng theevaluationof anexpo nentialfuncti on (oreach windowpixel included in the average.Because the upper limi t.;:."./1.1".is largerfortheweightedfilte r than forthe sigm a filter.moreof the higher valuepixels areincludedintheaveraging process.thereby removin gthelow bias inherentinthesigm a filterandreducingnoise more effect ively.Thelowerlimit .::...00theotherhand.ishigherforthe weighted filte rthanfor thesigma filter.This is an undesira ble feature of the weight ed filter becausesome low valuepixelswhichare withinthe two-sigmaran geofthecentral pixelvaluewillbeexcluded b,Y the weightedfilter.Some fine featu res whichwould have been preservedbythe sigmafilte rwill.therefore.be annihilated by theweighted filter.

Theweighted filter.whichisconsideredtobe anadvancedversionof theSigma filte r . takes theprobabilityoftbe centralpixel valueintO account todete rmi nethe ran ge forwhichtbe meanvaluecanbeextractedover afilter ing window.It repl aces thecentral pixelvaluewith tbe weightedmeanin thefilte ring window.inwhichthl' weightingcoefficients aredetermi nedbased00theprobabilityoftbepixe lvalue.The filteredimagebasa lar ger varianceinhomogeneousareascomparedwith theother filters.

Mod ifi edsigma filter

Twosimple modi fications tothesigmafilterhasbee nproposed by Sm it h(.tilin ord ertoimprovebothitscomputat ionalefficiencyanditsabilit)·to preserve fine features.Thefirst modificationassumesthemeanofapossibleGaussiandistribution contai ning the centra l anda givenwindowpixelto be a linearcombinat ionofthe

22

two.ratherthan equal to thecent ral pixelz(i.j)asinthestandardsigma filter.or equaltothe window pixelz(k. l)asintheweightedfil terof :\Iart inand Turner.The two-sigmarange of valuestobeincludedin theaveragingprocess is therebyincreased.

ena blingthe specklenoiseto be reduced moreeffecrtvely.and ensu ringthatmore fine feat urepixels aredetectedsothat fewer finefea tur esare annibilated.

Thesecond modificati onattempts to preservefine features incases in whichthe number of pixelswithi nthe two-sigmarangeisIt'S'>tbanor equaltothesharpspo t noise thresho ld. This isachieved by searching forthe Gaussian distri bu tion contai ning threeor more connected pixelsfromwhich tbecentralpixelismost likelytohave been displaced.

2.4.2 Edgedet ectio n

Oneof the major tasks in imageanalysisis edge detection.Edgesarefundamenta.ll~{

important primitivefeat uresofan image beca usetheyoft enprovid e an indicatio nof the physicalextentofobjectswithinthe image.An edge is definedas a variationor discontinuityinimage intensit yresulting from changesin some physicalproperties oftbe surface.nam ely.its reflectance.geometry and/orincidentillumination. Edges canalso be definedbased on changesinotbe r imagefeatures(e.g.texture).

In this subsec tion.wewillfirst brie8 yreviewedgedetectorsforSAR imag es.The n wewill focuson tbe :\.IaximumStrength EdgePruned Ratio of Averages(:\ISP- RoAJ method(18,191.whichwillbeused inmy proposedsegme ntation meth od.

SARedge detectors

:\Ianycommonedgedetectorsareof thegradient type i.e.thedetectorsarebased on thedifference between pixel values. This type ofedge detectorwhenapplied

23

to speckledimages can yield very poorresults becausespeckle ismulti p lica ti vein nature.:\Ioreover.the problem involvedwithspeckle is evenmoredifficultbeca use speckleisno t only mult iplicati ve.butcanalsohave an Inter-pixe lspatialcorr elation to some ext ent. Anefficient edge detectorshouldbe based on the specklemodel.

Hence.itisverydifficult todetectedges inspeckledimages and almostimpossible using simple gradientedgedet ectors.Sinceitisobv-ious thatspeckledependson the signal.speckle ismore prominentinhigherintens ityhom ogen eousarea.sthan in darker areas.Thus a ratio between pixelvaluesshould bea better edgeindicator than theirdifferentt.Whendealingwith nois}' images.itisbetterto take theratio ofthe averagepixelvaluesintwoadjacent neighbo rhoodsoppos itetothepixel of interest.A ratio magnitudeimageisthus formed and thresholding finallyprovides the edge map.Thisisthe basic ideabehin d thesim ple Ratio of Average(ROA) edge detector[8jwhic:b. ispanicu larl y useful fordetectiog edgesof imagescorru ptedwith a speckle noise.

Relativelyfew method shavebeen reported sofar in thisarea.despite the impo r- raneeof theproblemof edge detectionon speckleimagery.A simplemethodDarned theCoefficientof Variance(Co V) wasproposed(491basedontheConstantFalse Alarm Rate(CFAR)concept which uses acoefficient of variationwhichcan provide an edge st rengt h measure .Frost et 01(16Jproposed anedgedetect ing techniquefor SARimages,in whic:b.the maximumlikelihood ratio (LR)isthemeas ur e of edge str engt h.Themaximumlikelihoo d rat iois com puted basedon aSARimagemod el.

Bovik'sRatioof Average (RoA)[8Jisanother approach whichattemptstosolve this proble m.He suggested acombinat ionof theRoAandthe GaussianSmoothedlapla- cian(G St)meth ods.Accord ing toBovik,the RoAedgedetectorisquite efficienton speckle-degr ad ed imagesbuthas a drawbackof generatingvery thick edges.Onthe

24

otber hand .ageneraledgedetectorsuch as GSL gives fineedgesbutalsogivesrise tomany raise edges whiebisnotatalldesira ble.Acombinationorthesetwogives a much better resu ltthan eit heroftheindi vidu aledgedetectors.However.itis wort h mentioningthattheRoAedgedetectorisopt imal ifa pointwisemultiplicat ivemodel having eithernegativeexponentialorGaussianfirstorderstatistics.iscons idered.

~Ioreworkwhichissimplyan extension of theRoAdetectorisproposed byTouzi etc.[491. usingthe Constan t FalseAlarmRat e (CFAR)concept.

MSP-RoA edgedetector

Ratio-based edgedetectors esti mateedgestrengthat any pixelofinterestin animage by calculatingthe ratiobetween neighbowingpixelvalues.TheMaximumSt rengt h Edge Pru nedRatioofAvera ges(MSP-RoA)methoddevelopedin118.19J.isonesuch met bod which hasbeenshownto provideaccuratelocali zed edgema ps fromspeckled SARimages.At eachpixel intheimage.the methodcalculat esthe four rat io edge strengt hs

R,

=

min(P,/Q..Q./p.l. i~1.2.3.4 (2.12) correspo nding to tbefour usualorienta tions.as illustratedin Figur e 2.3.~rhereP,and Q. are the averagescalculatedoverthesub-windowsdenotedPand Q.respect ively.

TheMSP-RoA then calc ulatesavector(R.O )char acterizingapossible edgeatthat pixel.wherethecompo nentR=min(Rl•Rz•R;s,Fl.)istheedgestrengt hand 0isthe orientation which yieldsthe minimumR,value. Acand ida teedge pixelisclassi fied

11$anedgepixelifthemagnitudeR ::;Tr•forapresetthresholdTrE(0.I),andifR istbeminimummagnitudeof all thepixelsin asub-windowor(2D-+1)x 1pixels perpendiculartothe orientationO.

Test ingof theM:SP-RoA edgedetectoronairborne SAR images[19. IS}bas 25

~ ~

••

. . . .ftll",•• • _o<aIedtotl ~ '

•

^"::;.

" 11 ^.

"'-1' " ; _.

Figure2.3:~I S P·RoAsche me-

demonstratedittobe anefficie ntra tio- based edgedetect orwhichca n product'thin and accurate edgemaps inthepresence ofspecklenoise.

2.4.3 Histogram th resho ldi ng

After iterativeapplica tionofaspecklereducing filter.afilrerodima~I'«anI", ex- peeredtohave a muhimodalgray level hisrcgr a m su itable for rhreshol din g.Tsai proposed afas t histogrambasedalgorith m for multi- len"thres holdi ngPO!.andif wasdemonstratedtobemore powerfulthan thewid elyusedthreshold ingmethods basedon between-class varianceorI'ntro~·.Tsarsmethodassume'S that each desired class inthe image canberepresentedby an approximately hill-sha peddist rihuti on inthegray levelhistogram.Thefluc tuationsofan origin alhistogram aresmoothed byrecursivelyconvolvingthehistogram with aGaussian kernel sothar the desired peaks and valleysatvaryi ng levels or detailcanbe ob ta ined,Thedetected valleysin thesmoot hedhistogram indicat ethelocation ofthe thresholds.

26

(2.16) The proposedalgori thm assumesthat apeakshowninthe graylevelhistogram correspondstoa hom ogeneousregionoftheimageandthat avaiteyexistsbetween twoneighbori ngpeaks.Thechallengeistoloca te thebott omsof the valleysthat best separ atetheclasses. Letho(i)representthe num berofpixels intheoriginal imagewit h grayleveliforj

=

0.1....L.whereListhemaximumgraylevel.In a histogram.if

ho(i)>ho(1- 1) and ho{il>!loCi+I), i=1.2.".L-1 (2.13) thenapeakat grayleveljisdetect ed.Similarly.if

h,U)<h,U- 1)andh,U )<h,U+1), 1

=

^{1,2 . ...L-1 (2.1<)}

h,W<h,U-!)andh,U )=0, i=1.2...·.L (2.15) and thereexisttwo peaksatgraylevelsPiandprsuchthatPI<jand P2>j.then a valleyatgrayleveljisselect ed.Thedefinitionofpeaks andvalleysabove assumes thatthe peaks and valleyswillnot be presentatgray level0 andL.

The8uetuation ofthe originalgra)'level bistogram may generatemanyfalse peaksandva lleys. In orde rtofind theproperpeaksandvalleysat varyinglevels ofthreshold ing,weuseaGaussian kern elto smoot h thehistogram.Thedegree of smoothingismanip ulatedbythewidth of the Gauss iankerneland the num berof convolut ions. functionholt}.thenumberofpixelswithgray-levelt.isconvolved withaone dimensiona lGaussian kernelg{t.0")ofwidth0":

g(t,a)

=

a,5,,; - (-<'/2"') H(t,a),theconvolutionofho(t)andtheGaussian kernel.isdefinedas

H(t ,a)

=

ho(t).g(t,u)

= l:

^{ho(u )g(t- u.a)d u (2.17)}

27

For digital implementation.thedigitalGaussian kernel witha window size of 11"

=

3 is used to generate smoothing functions at various values ofa.and it isgiven by 9(-1 )~0.2261,9(0)=0.5478,9(1)~0.2261.

The discrete convolutionofho(i )and the digital Gaussian kernelg(u)isdefined

["'/2J

H(t,\1')

= L

ho(t

+

U)9(U) _e:=-[WI2J where[W/2]isthelar gestintegernot greate r thanW/2.

(2.18)

Afteriterati vefiltering with theGaussiankernel. the "alleys inthe histogram are then det ected.Pixelswhose gray levelslie between twoadjace ntva lleysareassigned tothe same region.Therefore,wehave dividedthefilteredimage intohomogenous regions and finishedthe segme ntation stage.

2.5 Concluding remarks

Thischapterattemptsto provide an overall pict ure of the previousworkmost closely related to theproblemofinterest.It focuses on the segmentation methods011which my research is based. Becausemy proposedmethod will be involvefiltering,edge detectionandhistogramthreshold ing. these related problems are also reviewed.The next cha pter \\;11provide my proposed edge-enhancedsegme ntation meth od for SAR images indetail.

28

Chapter 3

The proposed edge-enhanced segmentation method

3.1 Introduction

Myproposed edge-enhancedsegmentation meth od is based on theidea of Lee and Jur kevich[31Jthat repeat ed applicatio nof anedge-preservingspeckle reducingfilter willprovide animagewit h a multimodal histogram suitable forthreshold ing even if the originalimagehas aunimodal histogram.The problemsof Lee andJurkevich's method havebeen discussedin the previouschapter. Firstof all,due to the poor performanceofthefiltering algorit hm in edge areas,thesegmented image regions haveragged andinaccur at e regionsboundaries. Second,the input parameter(the ratio of the standa rd deviati on tomean)to thefiltering algorit hmshouldbe calculated manuallyandin their implementationthisparameter rema insuncha nged,though the physica l quan ti tyit describes(i.e.the speckle noise)may changeon each iteration.

Moreover,inthres holding,thehist ogram valleysare user-determined.Myapproach

29

basseveraladvantages over Leeand Jurkevich'smethod.Firstofall.edge informati on isusedwith the filteringalgorith ms. which leadstoa bet terperformanceofthe filters in edgeareas.Second.tbe iterati ve applicationofthefiltersisfullyauto matic.

whichmeansthat no stat istical parametersacerequiredas input para meters.as all theseparameters are automa ticallyestimated fromtheimage.Third.the histogram valleysace not manuallydeterminedinthe thresholdingalgorithm.Finally.due to the improvement in the filteringscheme.the segmentedimagehas simpleand accurate region boundariesand thereare no small holes in homogeneousregions.Thefollowing are the maincomponents ofmyapproach:

•UsingMSP-RoA edgedetect orto estimateedges

•Modifyinga specklereducing filterb}' using edgeinforma tionto improvethe filtering performancein edgeareas;

•Ite rativelyapplyingthemodifiedfilter(Icallthisfilteranedge-enhancedfilter) to smoot hthe specklenoise:

•Segmentingthefiltered imagebasedon histogramthresholdingtogetimage regions withaccurateregionboundaries.

3 .2 Main co mpone n t s

Themaincomponents of mysegme ntationmet hodincludeedge-enhan cedfilters.pa- ramet er estimatio n.edge detection.iterativeapplication.and histogram thresholding. These will be discussedintumin this section.

30

3.2 .1 Edge- enhancedfilt ers

Ofcrucial importanceinm~:segmentation methodisthe abilityofspeck lereduc- ing filters tosmoo th noise while preservin gthe sharp nessofedges.Severalspeckle smoot hing filters have been developed bypreviousresear chersas stated in thelitera - turereview.However.these filters usually donot use edgeinfonnation andsometi mes donotperf ormwell inedgeareas.In thisresearc h.I proposeanedge-enhanced filter- ing method whichut ilizes edgeinformation obtained bythe ),1$P·RoAedgedetector inlocal statistical analysis in thefilteringwindow.This kind offiltersmoot hsspeckle in lowvariance areas aswell asin high varianceareas while prese rvingthesharp ness ofedges.The startingpoint ofmyresearchwastomodify thesimplestmean and med ianfilter.withfair lygoodresults,some of which werepresented in(281.Theathe Leemultiplica ti vefilterwasmodifiedanditer ative ly applied tosmoothSARimages.

with someresu ltspresentedin(291.Theseresultsare evenbetterand havea signif- icant improvementovertheoriginaliterativeLeemultiplicativefilte r.especiall yin edgeareas.The segmentat ionresultsbased ontheedge-enhancedLeemultiplicative filter.in particular.areverygoodwhen testedon syntheticimagesaswellasreal SARimages.Detailedresults ofall methods arepresen ted andcom pa redin Chapte r -Iand5of thisthesis.

Edge-enhancedmeanandmedianfilters

Themain probl em with the mean filteris thatthe edges in theimage aresmoot hed together withthespeckle noise.and mayhe lostcompletelyaftera fewsuccessive applicat ionsofthe filter. Themedian 6.lterwillpreservestep edges innoise-free images.however,inspecklecorrupted images.it does notremovethisnon-impulsive noise very well.

31

In thisresearch.edge-enhanced mean andmed ian filtersare presentedwhich use the edgemap generatedbythe:\of$P·RoAedge detectortodeterminewhich of the neighbouring pixelsaretobeincluded in thecalcu la tionsof themeanor median.

Thismeth odtends to removethe specklenoise wh ile retaining the edgesevenafter several iterations.The followingisthefull descriptionof themodified localmeanand medianfilters.

First,the~1$P·RoAedge detec tor is applied to estim ate the edge points . Then.for each pixelz(i,j).a 3)( 3window centeredonthepixel is used foranalysis .Accordi ng totheesti mated. edge map.the pixelsinside the window canbedivided intotwo classes:those whichareedge pixels and thosewhichare Don-edge pixels.Theedge pixelsace excluded inthelocal mean ormediancalcu latio n.TheDensup is to decide ifz(i,j)isaedgepoint basedontheedge map.Ifz(i,j)isnot aedgepoint.then itis replacedby themean ofthenon-edgepixels.Otherwise,itisreplaced bythe median ofthe nee-edgepixels. After all the pixels inan image havebeenexami ned.then one itera tio nof thefilterisfinished.The process canberepeatedwithsubsequent iterations.

I chosesmallwindowsizesinthe edge-enhancedfiltersbecausea largewindow size may oversmooth some regions near oron edges.Theedge-enhancedmean and medianfiltersace simple and efficientforsegmentationpurpose.As"i ll be shownin Cha pter-I.even after severaliterat ions.mostedges in theimageare preservedwhile thenoise issignifican t ly reduced(28J. However.insome testimages.there are still some areas thataremerged.If there isagap in theedgemap.theedge tendsto blur when the edge-enhancedmean filter isapplied.

32

. 1/ - -

/j

Figur e 3.1:Usingedgeinfor ma tiontodefine the valid regioninthefiltering window

Edge-enhanced Lee multiplicative filter

Alt hough the origina l Lee mu lt iplicative filterperf or msverywell at noise smoo th ing in low varianceareas,it tendsto preserve speckle noise near edges.Since ratio-based edge detectors areable to ignor e speckle indetectingedges in SARimages.I usetilt' edge map produced by the :\ISP.R oAedge detector toimprove the performance of theLeemultiplicative filter.

By

usingthe edgeinforma tion obtained from the

!\fSP-RoA edge detector,

it

is

possibletorefine the definit ion of the local neighbourhoodoverwhich local statistics arecalcula ted,thus improving thehomogeneityof the neighb ou rhood and the qua lity of the estimates.The imp rovementin the performa nce of the Lee mult ip licative filter is mostnota ble in highvariance areas nearedges.Figure 3.1 showshowknowledgeof an edge contour is used indelimiting theneighbourhoodfor localstatisticalanalysis .

33

In a filtering window of a given size,all the pixels are classified as belonging to one of two classes,thus allowing the definitionof two regions wit hinthe window, the validand non-valid regions. The validregion starts with the centralpixelin the filtering window, and is grown in eight directions, along the arrows indicated in Figure 3.1 within a line of width one pixel. When the valid regionreaches an edge point as determined from the !vlSP-RoA edge map, or when it reachesthe window boundary,the region stops growing in that direction.In this way thefiltering window is separatedintotworegions andonly the pixelsinthevalidregion andalong the eight directionsare includedin stat ist icalestimation.

Inthe edge-enhancedLee multiplicativefilter,edge informationis thus utilized in definingthe filteringwindowsize andshape. Since

z

and

ver,

are calculatedas themeanandvariance onlyof thosepixels in the valid region they willyield more accurate est imatesnearan edge, so that themodifiedfiltercansmooth the speckle in edge areaswhilepreserving theshar pnessof edges.

Theiterat iveapplicationof the edge-enha ncedLee filterwas tested onsynthetic speckledimagesand theresults compa redwithsimilar ite rationsof theoriginalLee rnutiplicativefilter. Detailedresultsare presented in Chapter 4 and 5. Someof the results havebeen presented at a workshop[29]. The edge-e nhanced Lee filter performs better than Lee filter,especiall y in highvarianceregions,beca use it uses edge informationtorefine the filt er's local stat ist icsestimation.

3.2.2 Estimation of parameter a;

TheLeemulti plicat ive filterand the edge-enha ncedLee multip licativefilterrequire theknowledge of theratioofthestandarddeviat ionto meaninhomogeneous areas.

This pa ram et er is seen tobe(JIJforunitymean noise,and is someti mescalled the

34

Coefficient of Variation [CoV]. This value can be estimated by calculating sample values of Varzlz over several homogeneous or structure free areas of the observed image a. Alternatively, the value of the parameter

arcan be determined from the

known speckle characteristics of the number of looks and type of SAR image to be filtered (e.g. see

[31. 32]).

How

ever ,

the application

of a nonlinear filterca nsignificant ly alter t

he noise

characteristics, and it can be difficultto

analyti

cally

update the parameter

_{a v}

in an iterative filtering scheme.

In m)'implementa tion,al)is automatically

estimated from the

imagedat aby Smith'smet hod ,describedin

Section 2.3.2, thus better controlling the filter ing process

for

each pass of the filter .

Formoderat ely busy images,thelise of this estima ti on meth odtends tohighlightthelocal estimates

of

alJ

in

thehomogeneous regions, and toexclud e those

erro

neous

estimates ofa

vill

areas of

high variance such

as edge regions.

The automatic estimate of

allis

quite close to its theoretica l value. For example, the theoretical

v

al ue for -t-look airborne SAR images is around 0.25 {56]. and the

est ima ted values for the testing images are quite close to this value(refer to Chapter -I

and 5

).

The window sizes in estimating

al)

haw an

effect on the finalestimat ion.

In experiments, it is found that for the test images presented in this thesis. a '7x '7 window size can produce a good estimate of

al)'

Although estimation errors exist. till' Lee mult iplicat ive filter and the proposed

edge-enhancedLeemultiplicat ivefilter are not extremely

sensitive to the value of

O'v,

which means

that sma ll

estimation errors an.' tolera ble.

35

3.2 .3 Edge detection

Asnoted above,the edge map obtained bythe~lSP-RoAedge detector isusedin the edge-enhan ced filters.Theability of the edgedetectortofind true and accurat e edges while excludingfalse edgesis very important to the performance of thefilters.

The~lSP-RoAmethod makeseffective use of theinformation availablein rati o-based meth odsin achieving accurate,loca lized edgemaps withoutadditional edgethinning ope rat ions.

As with many otheredge detection met hods,the basic para metersof the~ISP­

RoA methodmust be determined

by

reference to a given imageand defined carefully illorder to exploittheadva ntagesof themeth od . The MSP- RoA perform an ce tends toimproveifthemask size is increased. provided theresult ingPand

Q

sub-areas arehomogeneous.Therefore,themasksize shouldbe selected aslarge as possible,by reference tothedistan ces betweenedges and thesizeof image objects in theimage. ThevalueT;canbeadjusted to detect as many significant edges as possible without detectingspurious or noise edges.The parameterDcanbeselected byconsidering the likely size of objects in theimage.Since the~ISP-RoAmethod prunes cand idate edgepixels within thesubwindow pependicular totheedge orientation,thevalu e of Dshould

be

small for imageshaving fine imagestructure.In this research,D is set to 1 for all the test images.

Testresul tsfrom someSARimages[18,19Jindicatethat theMSP-RoAmethod has difficulty in det ectin g alledgessuchas someedges in busyimage regions.Sincea la rge windowsize tend s to blursma ll regions,the 11SP-RoA meth odhasdifficul tyin detectingfinefeat ures whenabig windowsize is chosen.Choos ing asma llwindow willprod ucemore false edges due tothe noise art ifacts.Inthis resear ch,twomethods toimprovetheperformance of thebasicMSP-RoA method have beenused.First,

36

the:\olSP-RoAedgedet ectorisusedto gene rateanewedge mapbeforeeach itera ti ve application ofthe edge-eubaaeed filters.After each iteration.the,.{SP -Ro Awi ndow sizeisdecreased andthethreshold Tr increasedasthenoiseleveldecreases.so that itcan detectmoresignificant andfine edges. Secondandalternat ively.anoth erway toimprovetheperformanceof the basicM:SP-RoAmethodis tomodify thebasic'

~ISP· RoAmethod~.assigning weightstopixels intheavera ging process prod uci ng P,andQ.ineachorientatio n. Thepixelsnearest thecentral pixelare assigned more weight thanthosefurtherfrom the~ntraJpixel. iD ordernottodegr ade edge stre ngthforvery lineedges when alargewind ow sizeis used.Supposeawindowsize of(2n+1)x(2n+1) is used.theweightsareassignedasn. n -1.n -2....1 from the nearest pixeltothe furthest from thecentr alpixel.

The edge-enh ancedfiltersinthisthesisareusedtypicallywiththe)'ISP-RoA edgedetectorgenerat ing anew edgemapon eachiteration.However. sincethe\ISP- RoA edge detectorcanprod uce very good estimations ofedge maps for SARimages. evenwit h significantspecklenoise.wehavealsosim ply appliedthe ),ISP·RoAedge det ectoronce onlypriortofilterin gandthenre-used theresulton each iterat ion . thus savingcom puta tionaltime.

3.2.4 Numberofiterations

Thenumber ofapplicationsof thespecklesmoothingfilters has an effectonthe noise smoothingperform ance aswell as the final segmentationresults.Ingeneral.thenoise smoothingperform ancewillbebestatacert ai n iterati on.Toomany it era tionsmay oversmooththe testedimage.However.for segmentationpurposes.moreiterati ve applicat ions ofthe filters seemtoprod uceanimagethehistogramofwhich has deeper valleysandhigher peaks . asisdesira bleforhistogram thresholdi ng.More

37

detailed tests and discussion are provided in the next two chapters.

3.2.5 Hi stogram thresholding

After all iterative application of the edge-enhanced filters, the histogram of a filtered image typically has a

mut imoda l shape suitablefor thresholding.

Although there are many thresho

lding

schemes that have been proposed

.

the Gaussian smoothin

g

and valley seeking algorithm proposed by Tsa i is a good choice, as it is faster and more accurate than other widely used between-class variance and entropy method

s[50].

A detai

led description of Tsai's work has been provided in theliterat ure review in

Section 2.4.3. It is wort h mentioning that

in Tsai's work,

the number of classes in the

image should

be provided as an input parameter to cont rol the convolution

of the

Gaussian kernel. In my research,

I

use the number of

repea ted convolutions

as an

input parameter for the Gaussian

smoothing algorith

m.

The number of convolut

ion

is very important for the final segmentation stage

.

A large number of convolutions will produce fewer image regions in the result. More deta

iled

results and discussion will be provided in the next two chapters.

3.3 Overall approach

Figure 3.2 shows the flow diagram of my overall approach for segmenting SAR images.

The

iterative applicat ion

of the 1tSP-RoA edge detector and the modified filter is

called the edge-enhanced

filter. The MSP-RoA is appl

ied011

each

it era tion.

The threshold

Tr

is set to a value

between0.4-0.8in the first iteration(det ailed discussion

on the setting of the threshold value can be found

in[18, 19]).

After each application, this value increases by 0.025 (this was obta ined by testing

numerous

images) and

38

Figure3.2:Flowdiagramofoverallaproach

39