Superpixel-based composite kernel for hyperspectral image classification

We propose a superpixel-based composite kernel framework for hyperspectral image (HSI) classification. Composite kernel methods can utilize both the spectral and the spatial information for the HSI classification. However, setting the optimal spatial neighborhood for different spatial structures is a non-trivial issue. In order to adaptively exploit the spatial contextual information, we utilize superpixel to obtain spatial information. A superpixel can be regarded as a local neighborhood, whose size and shape can be adaptively adjusted according to the spatial structures in the HSI. Then, the spatial features are extracted by computing the mean of the spectral pixels within each superpixel. Finally, composite kernel with support vector machine is implemented on real HSI. Experiments on two real HSIs demonstrate the outstanding performance of the proposed method.