关键词: 遥感影像, 特征向量, 支持向量机, 面向对象, 自动分类

Abstract: In order to achieve automatic classification for high resolution remote sensing image and further improve the accuracy and efficiency of unsupervised classification, an object-oriented automatic classification method with automatic chosen train samples is presented in this paper. First, the image is segmented into homogeneous segments by using mean shift algorithm. Second, the multi-features of object segmentation are extracted （including spectral feature, texture feature and shape feature）. Based on the geometric distance of feature vector, the train samples are automatically chosen. Finally, the classification results are gotten through Support Vector Machine （SVM） classifier. The experiment result shows that the proposed algorithm of object-oriented automatic classification can make use of the rich feature of image object and better avoid the spiced salt phenomenon, so that both accuracy and efficiency of automatic classification have been much improved.

Key words: remote sensing image, feature vector, Support Vector Machine（SVM）, object-oriented, automatic classification