Hyperspectral Target Detection Based on Kernels

In this chapter, linear signal or target detection algorithms are extended to nonlinear versions by using kernel-based methods. In kernel-based methods, learning is implicitly performed in a high-dimensional feature space where high order correlation or nonlinearity within the data are exploited. Nonlinear realization is mainly pursued to reduce data complexity in a high-dimensional feature space and consequently provide simpler decision rules for data discrimination.

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