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.

Date Published: 10 October 2008
PDF: 9 pages
Proc. SPIE 7109, Image and Signal Processing for Remote Sensing XIV, 71090D (10 October 2008); doi: 10.1117/12.801735

Published in SPIE Proceedings Vol. 7109:
Image and Signal Processing for Remote Sensing XIV
Lorenzo Bruzzone; Claudia Notarnicola; Francesco Posa, Editor(s)