Multi-scale patch based box kernels for hyperspectral image classification

Integrating labeled pixels with prior knowledge of hyperspectral spatial homogeneous regions, we propose a region-based hyperspectral image classification method, called the support vector machine with the multi-scale patch based box kernel (SVM-MPBK). It models the local homogeneous region of each pixel as a box, and measures the similarity between different box regions using box kernel. The box is represented as multidimensional intervals computed band by band in a neighborhood pixel patch. Using multi-scale patches to calculate box, SVM-MPBK fuses the complementary classification results in different scales by a majority voting. Experimental results on benchmark hyperspectral data sets demonstrate the effectiveness of SVM-MPBK.