Comparison of kernel-based methods for spectral signature detection and classification of hyperspectral images

The aim of this paper is to assess and compare the performance of two kernel-based classification methods based on two different approaches. On one hand the Support Vector Machine (SVM), which in the last years has shown excellent results for hard classification of hyperspectral data, on the other hand a detection method called Kernel Orthogonal Subspace Projection KOSP, proposed in a recent paper.(1) To this aim, the widely used "Indian Pine" Aviris dataset is adopted, and a common "test protocol" has been considered: both methods have been tested adopting the one-vs-rest strategy, i.e. by performing the detection of each spectral signature (representing one of the N classes) and by considering the spectral signatures of the remaining N - I classes as background. The same dimensionality of the training set is also considered in both approaches.