CAJ | 학술논문

面向对象分类方法可以充分利用遥感影像的光谱和空间信息，是一种适合于高分辨遥感影像的分类方法。以2012年资源3号卫星高分辨率遥感影像（ZY －3）为数据源，对基于面向对象与最大似然监督分类的地类信息提取方法进行了对比分析。面向对象分析方法中采用改进后的局部方差法确定并选取不同地类类型的最优分割尺度，并采用多尺度层次的方法提取不同地类类型信息。结果表明：根据改进后的局部方差法确定的针叶林、阔叶林、针阔混交林地类类型的最优分割尺度为105；农田地类的最优分割尺度为105，水域、建筑类型的最优分割尺度为65。基于面向对象技术的地类信息提取方法其总体精度达到90.3％，Kappa 系数为0.82；最大似然法其总体精度为77.6％，Kappa 系数为0.71；基于面向对象方法的总体精度提高了12.7％，Kappa 系数提高了11％。表明了基于面向对象分析方法的地类信息提取在国产高分辨率影像上的适用性。同时，论文的研究也为森林资源调查中地类信息的遥感提取进行了有益的尝试。
면향대상분류방법가이충분이용요감영상적광보화공간신식，시일충괄합우고분변요감영상적분류방법。이2012년자원3호위성고분변솔요감영상（ZY －3）위수거원，대기우면향대상여최대사연감독분류적지류신식제취방법진행료대비분석。면향대상분석방법중채용개진후적국부방차법학정병선취불동지류류형적최우분할척도，병채용다척도층차적방법제취불동지류류형신식。결과표명：근거개진후적국부방차법학정적침협림、활협림、침활혼교임지류류형적최우분할척도위105；농전지류적최우분할척도위105，수역、건축류형적최우분할척도위65。기우면향대상기술적지류신식제취방법기총체정도체도90.3％，Kappa 계수위0.82；최대사연법기총체정도위77.6％，Kappa 계수위0.71；기우면향대상방법적총체정도제고료12.7％，Kappa 계수제고료11％。표명료기우면향대상분석방법적지류신식제취재국산고분변솔영상상적괄용성。동시，논문적연구야위삼림자원조사중지류신식적요감제취진행료유익적상시。
Object-oriented classification method which is suitable for the high resolution remote sensing images can make full use of the spectral and spatial information of remote sensing images.In this study, the information extraction method of object-oriented was compared to maximum likelihood method based on 2012 ZY-3 satellite high resolution remote sensing image.The results show that the optimal segmenta-tion scale of coniferous and broad-leaves and coniferous and broad-leaves mixed forest is 105,water and building is 65 according to improved local variance method.In object-oriented image analysis,the optimal segmentation scale of different land types was selected by improved local variance method,the ground fea-tures of different types were extracted in multi-scale level.The accuracy of high resolution remote sensing image information extraction based on object-oriented image analysis technology was 90.3%,kappa coef-ficient is 0.82;the accuracy of high resolution remote sensing image information extraction based on maxi-mum likelihood method was 77.6%,kappa coefficient is 0.71;the overall accuracy of the object-oriented image analysis technology is improved by12.7% and Kappa coefficient increases by 11%.It shows obvi-ously that the object-oriented image analysis technology can be applied to domestic high resolution image information extraction.This paper also attempts to extract land use information by remote sensing images in the investigation of forest resources.