一种面向对象的像元级遥感图像分类方法

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-Matusita 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。

A Hybrid Model of Object-Oriented and Pixel Based Classification of Remotely Sensed Data

Three classification methods： maximum likelihood at pixel level, maximum likelihood at object level and a hybrid model ofpixel 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.