Hyperspectral target detection using kernel orthogonal subspace projection
In this paper, a kernel-based nonlinear version of the orthogonal subspace projection (OSP) classifier is defined in terms of kernel functions. Input data is implicitly mapped into a high dimensional kernel feature space by a nonlinear mapping which is associated with a kernel function. The OSP expression is then derived in the feature space which is kernelized in terms of kernel functions in order to avoid explicit computation in the high dimensional feature space. The resulting kernelized OSP algorithm is equivalent to a nonlinear OSP in the original input space. Experimental results are presented for target detection in hyperspectral imagery and it is shown that the kernel OSP outperforms the conventional OSP classifier.