A Multiple-Mapping Kernel for Hyperspectral Image Classification
The kernel operate plays an necessary role in machine learning strategies like the support vector machine. During this letter, a new kernel framework is developed for hyperspectral image classification. In distinction to existing composite kernels created via a linearly weighted combination, the multiple-mapping kernel proposed in this letter is obtained through repeated nonlinear mappings. Experiments indicate that the proposed multiple-mapping kernel framework (MMKF) is effective for hyperspectral image classification. Compared to the only kernel ways, the MMKF tends to be a lot of advantageous in terms of classification accuracy, notably for the situation with a little-size coaching set.
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