CAJ | 학술논문

本文提出一种面向对象的像元级分类方法（混合模型），并将其与单纯的以像元和面向对象的两种方法同时应用于分辨率分别为30m和0.5m的环境星CCD数据和航空影像进行对比分析。分类结果中不同地物类别之间光谱可分性的大小，很大程度上可反映分类结果的可靠性。若地物类型之间的光谱差异大，说明分类方法能将光谱差异大的地物很好地划分，显示出较可靠的分类结果；相反，如果分类结果中地物类型光谱差异小，则反映分类方法不够可靠。鉴此，本文通过计算分类结果中不同类别所对应的原始遥感影像像元之间的J-M(Jeffries-Ma-tusita Distance)距离来度量分类结果中地物之间的光谱可分性，并用J-M距离比较分析了3种图像分类方法对2种不同分辨率影像的分类结果中各个类别之间的光谱可分性的变化。分析结果表明，混合模型不但能够得到较连续的分类结果，同时能够保持分类结果中类别之间的可分性。本文对分类结果进行了精度验证，结果发现混合模型的分类精度较其他2种方法要高。2种不同分辨率的遥感影像分析结果得到相同的结论，表明该模型适用于中分辨率和高分辨率影像。
본문제출일충면향대상적상원급분류방법（혼합모형），병장기여단순적이상원화면향대상적량충방법동시응용우분변솔분별위30m화0.5m적배경성CCD수거화항공영상진행대비분석。분류결과중불동지물유별지간광보가분성적대소，흔대정도상가반영분류결과적가고성。약지물류형지간적광보차이대，설명분류방법능장광보차이대적지물흔호지화분，현시출교가고적분류결과；상반，여과분류결과중지물류형광보차이소，칙반영분류방법불구가고。감차，본문통과계산분류결과중불동유별소대응적원시요감영상상원지간적J-M(Jeffries-Ma-tusita Distance)거리래도량분류결과중지물지간적광보가분성，병용J-M거리비교분석료3충도상분류방법대2충불동분변솔영상적분류결과중각개유별지간적광보가분성적변화。분석결과표명，혼합모형불단능구득도교련속적분류결과，동시능구보지분류결과중유별지간적가분성。본문대분류결과진행료정도험증，결과발현혼합모형적분류정도교기타2충방법요고。2충불동분변솔적요감영상분석결과득도상동적결론，표명해모형괄용우중분변솔화고분변솔영상。
Three classification methods:maximum likelihood at pixel level, maximum likelihood at object level and a hybrid model of pixel based and object-based classification method were used to classify these two sorts of images, i.e., 30m resolution HJ-1-A CCD imagery and 0.5 m resolution aerial images. Spectral separability in training samples between different classes directly affect the classification accuracy;on the contrary, spectral sep-arability between different classes in the classification result can be used to evaluate the accuracy of classifica-tion. Based on this assumption, classification results of these three methods were evaluated by spectral separabili-ty between different classes. Result reveals that compared with the maximum likelihood at object level, classifi-cation at pixel level can make full use of the spectral information in remotely sensed image. Analysis also shows that hybrid model can improve the classification accuracy. Multi-resolution image analysis proves that the hybrid model is suitable for applications at different scales.