简介:Specklefilteringofsyntheticapertureradar(SAR)imageswhilepreservingthespatialsignalvariability(textureandfinestructures)stillremainsachallenge.ManyalgorithmshavebeenproposedfortheSARimagerydespeckling.However,simulatedannealing(SA)methodisoneofexcellentchoicescurrently.AcriticalprobleminthestudyonSAistoprovideappropriatecoolingschedulesthatensurefastconvergencetonear-optimalsolutions.Thispapergivesanewnecessaryandsufficientconditionforthecoolingschedulesothatthealgorithmstateconvergesinallprobabilitytothesetofgloballyminimumcoststates.Moreover,itconstructsanappropriateobjectivefunctionforSARimagedespeckling.AnexperimentalresultoftheactualSARimageprocessingisobtained.
简介:ThereisdifficultyfordistinguishingofriverandshadowinSyntheticApertureRadar(SAR)images.AmethodofriversegmentationinSARimagesbasedonwaveletenergyandgradientisproposedinthispaper.Itmainlyincludestwoalgorithms:coarsesegmentationandrefinedsegmen-tation.Firstly,Theriverregionsarecoarselysegmentedbythewaveletenergyfeature,andthenrefinedsegmentedaccuratelybythegradientthresholdwhichisgotadaptively.Theexperimentalresultsshowthevalidityofthemethod,whichprovidesagoodfoundationfortargetsdetectionabovetheriver.
简介:ThemathematicalmodelofspaceborneSARsystemsanditscomputersimulationsaredescribed.Resultsofcomputersimulationsaboutrangemigration,rangemigrationcorrection,azimuthweightingandsignalgeneration/processingaregiven.ThissoftwarecanbeusedtosimulatethedynamicprocessesinspaceborneSARsystems,todevelopnewsignalprocessingtechniquesandtoevaluatetheperformanceofthedesignedsystem.
简介:弹载SAR岛屿海岸线景象匹配成功概率与实时图中海岸线形状密切相关,海岛环境下海浪和地形起伏等因素对海岸线在SAR图像中的特征具有不可忽略的影响,造成海岸线的模糊和变形。首先分析了海岛环境弹载SAR图像的特点,然后基于海浪频谱和方向谱相关经验公式,利用线性叠加方法给出了详细的海浪仿真的实现过程;建立了海岛高程起伏的二维指数函数模型;利用波数域方法给出了典型场景下的海岛SAR图像仿真结果,并在此基础上分析了海浪和山峰对弹载SAR图像海岸线检测的影响和基于SAR图像的海岸线检测方法局限性。研究结果表明基于SAR回波数据的海岸线检测方法是一种适于弹载SAR应用的有效方法。
简介:ThefeasibilityofERSSARTandemdataformappingforestandnon-forestcoverinChinawasevaluatedoverZengchengCountyintheSouthChina.Anaccuracyof75%hasbeenachieved.Then,theMACFERST(MappingChinaForestwithERSSARTandemdata)projectstartedbytheMinistryofScienceandTechnology(MOST)ofChinaandtheEuropeanSpaceAgency(ESA)in1999.Thegenerationofalarge-scaleforestmaprequiressolvingproblemssuchasthegeoreferencingandmosaickingofverylongimagestripscov...
简介:ThispaperstudiesSyntheticApertureRadar(SAR)imagescontaminatedbythecoherentspecklenoisewiththemultiresolutionanalysisofwavelettransform.ThisstudyshowsthattheinfluencesofthespeckleondifferentfrequencycomponentsoftheSARimagearedifferent,andthattheSARimageandthespecklehavedifferentmannersofsingularity.So,thispaperpresentsadenoisingmethodofwaveletanalysistoreducethespeckle.Someexperimentsapprovethatthismethodnotonlysuppressesthespeckleeffectively,butalsopreservesasmuchtargetcharacteristicsoftheoriginalimageaspossible.ItshowsthatthisdenoisingmethodofwaveletanalysisoffersaveryattractivealternativetosuppressthecoherentspecklenoiseoftheSARimage.
简介:Whentheclassicalconstantfalse-alarmrate(CFAR)combinedwithfuzzyC-means(FCM)algorithmisappliedtotargetdetectioninsyntheticapertureradar(SAR)imageswithcomplexbackground,CFARrequiresblock-by-blockestimationofcluttermodelsandFCMclusteringconvergestolocaloptimum.Toaddresstheseproblems,thispaperpro-posesanewdetectionalgorithm:knowledge-basedcombinedwithimprovedgeneticalgorithm-fuzzyC-means(GA-FCM)algorithm.Firstly,thealgorithmtakestargetregion’smaximumandaverageintensity,area,lengthoflongaxisandlong-to-shortaxisratiooftheexternalellipseasfactorswhichinfluencethetargetappearingprobability.Theknowledge-baseddetectionalgorithmcanproducepreprocessresultswithouttheneedofestimationofcluttermodelsasCFARdoes.AfterwardtheGA-FCMalgorithmisimprovedtoclusterpre-processresults.IthasadvantagesofincorporatingglobaloptimizingabilityofGAandlocaloptimizingabilityofFCM,whichwillfurthereliminatefalsealarmsandgetbetterresults.TheeffectivenessoftheproposedtechniqueisexperimentallyvalidatedwithrealSARimages.
简介:ThisletterstudiesonthedetectionoftexturefeaturesinSyntheticApertureRadar(SAR)images.ThroughanalyzingthefeaturedetectionmethodproposedbyLopes,animprovedtexturedetectionmethodisproposed,whichcannotonlydetecttheedgeandlinesbutalsoavoidstretchingedgeandsuppressinglinesoftheformeralgorithm.ExperimentalresultswithbothsimulatedandrealSARimagesverifytheadvantageandpracticabilityoftheimprovedmethod.