二○一一届毕业设计面向对象的高分辨率遥感影像分类Object-orientedClassificationofhighResolutionRemoteSensingimages

学院：地质工程与测绘学院专业：遥感科学与技术姓名：学号：指导教师：完成时间：2011年6月17日

二〇一一年七月摘要高空间分辨率遥感影像使得在较小的空间尺度上观察地表细节变化，进行大比例尺遥感制图，以及监测人为活动对环境的影响成为可能。随着高分辨率影像的应用越来越普及，迫切要求人们对高分辨率遥感信息提取进行研究，以满足高分辨率影像信息不断增长的应用和研究需要高分辨率遥感影像光谱信息有限，空间信息丰富，地物的尺寸、形状及相邻地物间的关系都得到很好的反映。面向对象的分类方法与传统的基于像素的分类相比，不仅仅是依靠光谱信息，而且还充分利用影像的空间信息，分类时也克服了基于像元的逐点分类无法对相同语义特征的像素集合进行识别的缺点，是一种目前最适合于高分辨率遥感影像的分类方法。本文采用面向对象的分类方法对高分辨率影像进行分类，该方法首先对影像进行多尺度分割获得同质区域对象，在此基础上利用模糊分类思想对分割后的对象进行分类。该方法不仅充分利用了高分辨率影像的空间信息，还将基于像素的分类提升到了基于对象的分类。多尺度分割采用的是区域生长合并算法，通过对尺度阈值、光谱因子及形状因子等参数的控制，可以获得不同尺度下有意义的对象。分割后的对象不仅包含了原始的光谱信息，还可以提供大量辅助特征，如纹理、形状、拓扑等特征。综合利用这些特征以及模糊分类的思想，使得高分辨率影像分类在减少分类不确定性的同时，还提高了分类的精度。最后将面向对象分类结果与传统的基于像素分类结果进行对比分析，发现其分类精度要明显高于传统法，且具有较强的抗噪声的功能，分类所得的地物结果相对较为完整，具有更丰富的语义信息，更加符合客观现实情形。

关键词：高分辨率遥感影像，面向对象的分类，影像分割，多尺度，最近邻分类AbstractWiththeapplicationofthehigh-resolutionimagemoreandmorepopular,itisurgentlyrequirepeopletocarryonresearchtoclassificationofthehigh-resolutionremotesensinginordertomeettheincreasingapplicationandstudyrequirementoftheinformationofhigh-resolutionimages.However,weusethetraditionalpixel-orientedmethodtoclassifythehigh-resolutionremotesensingimage,itcan’tfullyutilizeimageinformationweshouldreducetheprecisionofclassificationandhasslowspeed.Accordingtothecharacteristicofthehigh-resolutionremotesensingimage,thepaperproposestousetheobject-orientedmethodtoclassifyhigh-resolutionremotelysenseddata.Thispapermakesuseoftheobject-orientedapproachtotheclassificationofhigh-resolutionimagery,involvesthesegmentationofimagedataintoobjectsatmultiplescalelevels.Classrulesaregeneratedusingspectralsignatures,shapeandcontextualrelationships,andthenusedasabasisforthefuzzyclassificationoftheimagery.Theobjectisderivedbymeansofmulti-scalesegmentationinthispaper.Thehierarchicalimagesegmentationandregion-mergingareimplemented.Asidefromthespectralvaluesofthepixels,theshapeoftheobjectscreatedbythepixelsandtherelationshipsbetweentheobjects,arealsoconsideredduringtheclassification.Theutilizationofspectral,textural,shapepropertiesandfuzzythinkingmayreducetheuncertaintyintheprocessofclassification.Acomparisonoftheresultsshowsbetteroverallaccuracyoftheobject-orientedclassificationoverthepixel-basedclassification.Thisconclusionindicatesthatobject-orientedanalysishasgreatpotentialforextractinglandcoverinformationfromsatelliteimagery.KeyWords:high-resolutionimagery;object-orientedclassification;imagesegmentation;multi-scale;nearestneighbourclassification目录摘要................................................................................................................................2Abstract..........................................................................................................................3目录................................................................................................................................4第一章绪论..............................................................................................................51.1研究背景..........................................................................................................51.2面向对象的遥感影像分类方法的提出..........................................................61.2.1基于像元的分类方法...........................................................................61.2.2面向对象遥感影像分类方法的提出...................................................81.3面向对象遥感影像分类国内外研究现状......................................................81.3.1国际上面向对象遥感影像分类方法研究...........................................81.3.2国内面向对象遥感影像分类方法研究.............................................101.4研究方法和内容............................................................................................10第二章面向对象的遥感影像分类..........................................................................112.1影像分割........................................................................................................112.1.1多尺度影像分割.................................................................................12多尺度分割参数的选择..............................................................................122.2影像分类........................................................................................................172.2.1最邻近分类.........................................................................................172.2.2决策支持的模糊分类.........................................................................18第三章实验数据与研究方法..................................................................................193.1实验数据........................................................................................................213.2软件平台........................................................................................................213.3实验区信息提取实验....................................................................................223.3.1图像分割.............................................................................................223.3.2影像分类.............................................................................................263.3.3基于像元的影像分类.................................................................................31第四章分类结果评价............................................................................................324.1两种分类结果精度评价................................................................................32第五章结论与讨论................................................................................................345.1结论................................................................................................................345.2讨论................................................................................................................35参考文献......................................................................................................................36