In this paper, a method for solving the nonlinear problem of classifying Multi-band Remote Sensing Images is proposed. By introducing the concept of Kernel space, the classification, which cannot be performed linearly in the input space, can be mapped into a high-dimension space, in which the problem can be solved linearly. Moreover, by using Kernel Function, the complex computation in the high-dimension space can be avoided. Based on this method, this paper improved a simple classification method called adaptive min-distance algorithm, and applied it to the classification of multi-band remote sensing images. A choice heuristic is also presented to select an appropriate Kernel Function. Experiments show that, with higher accuracy achieved, the improvements prove to be useful in the classification.
刘伟强, 胡静, 夏德深. 基于核空间的多光谱遥感图像分类方法[J]. 国土资源遥感, 2002, 14(3): 44-47,57.
LIU Wei-Qiang, HU Jing, XIA De-Shen . CLASSIFICATION OF KERNEL BASED ON MULTI-BAND REMOTE SENSING IMAGES. REMOTE SENSING FOR LAND & RESOURCES, 2002, 14(3): 44-47,57.
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