多尺度分割原理與應用

HierarchicalSegmentationReadingReportHSegCONTENTSPARTONEAbouttheIntroduction.PARTTWOTheHsegSegmentation.PARTTHREEApplicationinENVI.1PARTONEAbouttheIntroduction.AbstractRecentadvancesinspectral–spatialclassificationofhyperspectralimagesarepresentedinthispaper.Severaltechniquesareinvestigatedforcombiningbothspatialandspectralinformation.Highlighttheimportanceofspectral–spatialstrategiesfortheaccurateclassificationofhyperspectralimagesandvalidatetheproposedmethods.IntroductionAdvantagesofhyperspectralremotesensortechnology:Thedetailedspectralinformationincreasesthepossibilityofmoreaccuratelydiscriminatingmaterialsofinterest.Thefinespatialresolutionofthesensorsenablestheanalysisofsmallspatialstructuresintheimage.Manyoperationalimagingsystemsarecurrentlyavailableprovidingalargeamountofimagesforvariousthematicapplications.But,italsobringssomeproblem:theCurseofDimensionalityandtheneedforspecificspectral–spatialclassifiers.PS:維數(shù)災難(CurseofDimensionality):通常是指在涉及到向量的計算的問題中，隨著維數(shù)的增加，計算量呈指數(shù)倍增長的一種現(xiàn)象。維數(shù)災難在很多學科中都可以碰到，比如動態(tài)規(guī)劃，模式識別和影像識別等。IntroductionTheCurseofDimensionalityofhyperspectralremotesensortechnology:Inhigh-dimensionalspaces,normallydistributeddatahaveatendencytoconcentrateinthetails,

whichseemstobecontradictorywithitsbell-shapeddensityfunction.therateofconvergenceofthestatisticalestimationdecreaseswhenthedimensiongrowswhileconjointlythenumberofparameterstoestimateincreases,makingtheestimationofthemodelparametersverydifficult.withalimitedtrainingset,beyondacertainlimit,theclassificationaccuracyactuallydecreasesasthenumberoffeaturesincreases.IntroductionHowtobuildaccurateclassifiersforhyperspectralimages?SVMsperformanonlinearpixel-wiseclassificationbasedonthefullspectralinformationwhichisrobusttothespectraldimensionofhyperspectralimages.IterativestatisticalclassifierbasedonMarkovrandomfield(MRF)modeling.NotethatrecentlyadaptiveMRFhavebeenintroducedinremotesensing.Useadvancedmorphologicalfiltersasanalternativewayofperformingjointclassification.PS:魯棒性(Robust):即系統(tǒng)的健壯性，是在異常和危險情況下系統(tǒng)生存的關鍵。Introduction2PARTTWOTheHsegSegmentation.TheHsegSegmentationTheHSegalgorithmisasegmentationtechniquecombiningregiongrowing,usingthehierarchicalstepwiseoptimization(HSWO)method,whichproducesspatiallyconnectedregions,withunsupervisedclassification,thatgroupstogethersimilarspatiallydisjointregions.Thealgorithmcanbesummarizedasfollows.Initialization:Initializethesegmentationbyassigningeachpixelaregionlabel.Ifapresegmentationisprovided,labeleachpixelaccordingly.Otherwise,labeleachpixelasaseparateregion.TheHsegSegmentationIIIIIIIVCalculatethedissimilaritycriterionvaluebetweenallpairsofspatiallyadjacentregionsStopifconvergenceisachieved.Otherwise,returntostepIMergespatiallynonadjacentregionsMergespatiallyadjacentregionsTheHsegSegmentationI計算每一對空間相鄰區(qū)域的相異準則值（dissimilaritycriterionvalue），例如計算向量?；蛘邊^(qū)域平均向量間的光譜角填圖SAM。SAM是基于物理的一種光譜分類，利用n維角度來匹配兩組像元光譜，將光譜看成是維數(shù)與波段數(shù)相等的空間里的向量，計算光譜間的角度的算法，決定了兩個光譜之間的相似性，也就是通過計算兩向量之間的廣義角來確定它們的相似性（角度越小越相似）。這種方法充分利用了光譜維的信息。TheHsegSegmentationII找到最小的相異準則值并設為閾值，如果兩相鄰區(qū)域的相異準則值等于閾值則合并這兩個區(qū)域。即dissim_val=thresh_val時，合并兩相鄰區(qū)域。TheHsegSegmentationIII如果Swght>0.0，則將dissim_val<Swght*thresh_val的不相鄰區(qū)域合并（Swght是衡量基于光譜信息的聚類相對于區(qū)域增長的相對重要性的選擇性參數(shù)，當Swght=0.0時，僅空間上相鄰的區(qū)域才允許合并，

當0<Swght<1時，則空間相鄰區(qū)域相對于不相鄰區(qū)域有一1/Swght的系數(shù)更可能被合并）TheHsegSegmentationIV如果合并完成則結(jié)束，否則回到步驟I。通過迭代運算，最終形成從細到粗的多尺度、多層次的分割結(jié)果。TheHsegSegmentation因為要對空間上非相鄰的區(qū)域進行合并，運算量變得非常巨大。為解決運算量問題，發(fā)展出了迭代分而治之的多尺度分割近似計算及其相應的有效實現(xiàn)方式。多尺度分割能輸出一個從初始化分割直至一個區(qū)域的分割的多尺度序列，在這個序列中，一個特定的對象既可以表示成幾個區(qū)域，從而具有較好的細節(jié)信息，也可以與其他對象一起被一個區(qū)域吸收。在實際