期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B2
页码：1011-1014
出版社：Copernicus Publications
摘要：Automatic image categorization using low-level features is a challenging research topic in remote sensing application. In this paper, we formulate the image categorization problem as an image texture learning problem by viewing an image as a collection of regions, each obtained from image segmentation. Our approach performs an effective feature mapping through a chosen metric distance function. Thus the segmentation problem becomes solvable by a regular classification algorithm. Sparse SVM is adopted to dramatically reduce the regions that are needed to classify images. The selected regions by a sparse SVM approximate to the target concepts in the traditional diverse density framework. The proposed approach is a lot more efficient in computation and less sensitive to the class label uncertainty. Experimental results are included to demonstrate the effectiveness and robustness of the proposed method
