对判读区域自然景观复杂,数据时相与质量差异较大的遥感图像用常规的分类方法难以达到令人满意的效果,为此,作者采用了一种通过定义图像判读区,分类管理器和改进监督分类算法等方法来实现遥感图像的分区自动分类,以不同时相的TM拼接图像进行分类试验,结果表明:该方法比传统的监督分类方法有明显改进:(1)与整幅图像用同一个标准进行分类的方案相比,其精度显著提高,(2)可在分类前灵活,任意生成所感兴趣的判读区域,(3)在每个分区内可以采取不同的分类方案进行分类,(4)每个分区的分类结果可以保存在同一个文件中,而不需要另外生成新的操作层.因此分类不受次数的限制,可保证分类结果的完整性,每个分区的分类结果也可以保存为单个分区的分类结果.

For the sake of the complicated factor of objects in remote sensing images,"homo spectrum"and"hetero spectrum"co exist usually in remote sensed imagery.However,most traditional supervised merhods take the same classification criteria by spectral statistic properties for various objects in the same image file.This kind of processing influence the accuracy of classification,especially for those images which have the special characteristics such as,complicated scenes,or many differences between temporal and quality of images. For this reason,the authors put forward and have realized an approach for segmentation based classification to solve this problem.The primary procedures are completed by defining the interpretation area and classification manager,and improving the supervised classification algorithm using visual C++ 6.0 language program. Finally,the authors used TM image mosaiced by two scenes,which acquired in two different time for the neighborhood areas,and then implemented the segmentation based classification to do the experiments.The results for this experiment show: (1) The precision using segmentation based classification is obviously improved in comparing with the same schema for the whole image. (2) The interpretation area can be randomly chosen and easily obtained for the sub areas before classification according to the features of images. (3) This method can help users to choose the different schemas for classification according to the properties of the each sub areas freely. (4) This method provides the storage strategy for the classification results,for all sub areas can be stored in one file,or in different files respectively,while it is not necessary to create a new layer to store the file for the results. In short,segmentation based classification for remotely sensed imagery is feasible to classify the imageries which have"homo spectrum"and"hetero spectrum"properties,and to improve the accuracy of by the classification for every sub area divided according to imagery properties.