Abstract: With the development of remote sensing and the related techniques, the resolution of these images is largely improved. Compared with moderate or low resolution images, highresolution images can provide more detailed ground information. However, a variety of terrain has complex spatial distribution. The different objectives of highresolution images have a variety of features. The effectiveness of these features is not the same, and some of them are complementary. Considering the above characteristics, a new method is proposed to classify remote sensing images based on the hierarchical fusion of multifeature. Firstly, these images are preclassified into two categories in terms of whether feature points are uniform or nonuniform distributed. Then, the color histogram and Gabor texture feature are extracted from the uniform distributed categories, and the ScSPM（linear spatial pyramid matching using sparse coding）feature is obtained from the nonuniform distributed categories. Finally, the classification is performed by the two different support vector machine classifiers. The experimental results on a large remote sensing image database with 2100 images show that the overall classification accuracy is boosted by 10% in comparison with the highest accuracy of single feature. Compared with other methods of multiple features fusion, the proposed method has achieved the highest classification accuracy which has reached 90.1%, and the time complexity of the algorithm is also greatly reduced.
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Key words: remote sensing image, color histogram, Gabor texture feature, ScSPM, multifeature fusion