The Orthogonal Subspace Projection (OSP) algorithm is substantially a kind of matched filter that requires the evaluation of a prototype for each class to be detected. The kernel OSP (KOSP) has recently demonstrated improved results for target detection in hyperspectral images. The use of kernel methods helps to combat the high dimensionality problem and makes the method robust to noise. This paper incorporates the contextual information to KOSP with a family of composite kernels of tunable

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... lexity. The good performance of the proposed methods is illustrated in hyperspectral image target detection problems. The information contained in the kernel and the induced kernel mappings is analyzed, and bounds on generalization performance are given.