多尺度分割原理与应用

HierarchicalSegmentationReadingReportHSegCONTENTSPARTONEAbouttheIntroduction.PARTTWOTheHsegSegmentation.PARTTHREEApplicationinENVI.1PARTONEAbouttheIntroduction.AbstractRecentadvancesinspectral–spatialclassificationofhyperspectralimagesarepresentedinthispaper.Severaltechniquesareinvestigatedforcombiningbothspatialandspectralinformation.Highlighttheimportanceofspectral–spatialstrategiesfortheaccurateclassificationofhyperspectralimagesandvalidatetheproposedmethods.IntroductionAdvantagesofhyperspectralremotesensortechnology:Thedetailedspectralinformationincreasesthepossibilityofmoreaccuratelydiscriminatingmaterialsofinterest.Thefinespatialresolutionofthesensorsenablestheanalysisofsmallspatialstructuresintheimage.Manyoperationalimagingsystemsarecurrentlyavailableprovidingalargeamountofimagesforvariousthematicapplications.But,italsobringssomeproblem:theCurseofDimensionalityandtheneedforspecificspectral–spatialclassifiers.PS:维数灾难(CurseofDimensionality):通常是指在涉及到向量的计算的问题中，随着维数的增加，计算量呈指数倍增长的一种现象。维数灾难在很多学科中都可以碰到，比如动态规划，模式识别和影像识别等。IntroductionTheCurseofDimensionalityofhyperspectralremotesensortechnology:Inhigh-dimensionalspaces,normallydistributeddatahaveatendencytoconcentrateinthetails,

whichseemstobecontradictorywithitsbell-shapeddensityfunction.therateofconvergenceofthestatisticalestimationdecreaseswhenthedimensiongrowswhileconjointlythenumberofparameterstoestimateincreases,makingtheestimationofthemodelparametersverydifficult.withalimitedtrainingset,beyondacertainlimit,theclassificationaccuracyactuallydecreasesasthenumberoffeaturesincreases.IntroductionHowtobuildaccurateclassifiersforhyperspectralimages?SVMsperformanonlinearpixel-wiseclassificationbasedonthefullspectralinformationwhichisrobusttothespectraldimensionofhyperspectralimages.IterativestatisticalclassifierbasedonMarkovrandomfield(MRF)modeling.NotethatrecentlyadaptiveMRFhavebeenintroducedinremotesensing.Useadvancedmorphologicalfiltersasanalternativewayofperformingjointclassification.PS:鲁棒性(Robust):即系统的健壮性，是在异常和危险情况下系统生存的关键。Introduction2PARTTWOTheHsegSegmentation.TheHsegSegmentationTheHSegalgorithmisasegmentationtechniquecombiningregiongrowing,usingthehierarchicalstepwiseoptimization(HSWO)method,whichproducesspatiallyconnectedregions,withunsupervisedclassification,thatgroupstogethersimilarspatiallydisjointregions.Thealgorithmcanbesummarizedasfollows.Initialization:Initializethesegmentationbyassigningeachpixelaregionlabel.Ifapresegmentationisprovided,labeleachpixelaccordingly.Otherwise,labeleachpixelasaseparateregion.TheHsegSegmentationIIIIIIIVCalculatethedissimilaritycriterionvaluebetweenallpairsofspatiallyadjacentregionsStopifconvergenceisachieved.Otherwise,returntostepIMergespatiallynonadjacentregionsMergespatiallyadjacentregionsTheHsegSegmentationI计算每一对空间相邻区域的相异准则值（dissimilaritycriterionvalue），例如计算向量模或者区域平均向量间的光谱角填图SAM。SAM是基于物理的一种光谱分类，利用n维角度来匹配两组像元光谱，将光谱看成是维数与波段数相等的空间里的向量，计算光谱间的角度的算法，决定了两个光谱之间的相似性，也就是通过计算两向量之间的广义角来确定它们的相似性（角度越小越相似）。这种方法充分利用了光谱维的信息。TheHsegSegmentationII找到最小的相异准则值并设为阈值，如果两相邻区域的相异准则值等于阈值则合并这两个区域。即dissim_val=thresh_val时，合并两相邻区域。TheHsegSegmentationIII如果Swght>0.0，则将dissim_val<Swght*thresh_val的不相邻区域合并（Swght是衡量基于光谱信息的聚类相对于区域增长的相对重要性的选择性参数，当Swght=0.0时，仅空间上相邻的区域才允许合并，

当0<Swght<1时，则空间相邻区域相对于不相邻区域有一1/Swght的系数更可能被合并）TheHsegSegmentationIV如果合并完成则结束，否则回到步骤I。通过迭代运算，最终形成从细到粗的多尺度、多层次的分割结果。TheHsegSegmentation因为要对空间上非相邻的区域进行合并，运算量变得非常巨大。为解决运算量问题，发展出了迭代分而治之的多尺度分割近似计算及其相应的有效实现方式。多尺度分割能输出一个从初始化分割直至一个区域的分割的多尺度序列，在这个序列中，一个特定的对象既可以表示成几个区域，从而具有较好的细节信息，也可以与其他对象一起被一个区域吸收。在实际