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Automatic target generation process (ATGP) has been widely used for unsupervised hyperspectral target detection. It implements a succession of orthogonal subspace projections (OSPs) to extract targets of interest without prior knowledge. This paper extends ATGP to a kernel version of ATGP, called kernel ATGP (KATGP) to further deal with linear non-separation problem. It introduces nonlinear kernels...
This paper presents a new approach to unsupervised classification for multispectral imagery. It first implements the pixel purity index (PPI) which is commonly used in hyperspectral imaging for endmember extraction to find seed samples without prior knowledge, then uses the PPI-found samples as support vectors for a kernel-based support vector machine (SVM) to generate a set of initial training samples...
Support vector machine (SVM) has received considerable interest in hyperspectral image classification. In order to make SVM work effectively one challenge is selection of training samples. In supervised classification it is generally done by random sampling for cross validation where two issues must be addressed. One is how many training samples required to allow SVM to produce good performance and...
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