Target detection with a contextual kernel orthogonal subspace projection

Luca Capobianco; Gustavo Camps-Valls

doi:10.1117/12.801735

10 October 2008 Target detection with a contextual kernel orthogonal subspace projection

Proceedings Volume 7109, Image and Signal Processing for Remote Sensing XIV; 71090D (2008) https://doi.org/10.1117/12.801735
Event: SPIE Remote Sensing, 2008, Cardiff, Wales, United Kingdom

Abstract

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 complexity. 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.

Citation Download Citation

Luca Capobianco and Gustavo Camps-Valls "Target detection with a contextual kernel orthogonal subspace projection", Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 71090D (10 October 2008); https://doi.org/10.1117/12.801735

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