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Báo cáo hóa học: " Research Article A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: Research Article A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery | Hindawi Publishing Corporation EURASIP Journal on Advances in Signal Processing Volume 2007 Article ID 29250 13 pages doi 10.1155 2007 29250 Research Article A Comparative Analysis of Kernel Subspace Target Detectors for Hyperspectral Imagery Heesung Kwon and Nasser M. Nasrabadi US Army Research Laboratory ATTN AMSRL-SE-SE 2800 Powder Mill Road Adelphi MD 20783-1197 USA Received 30 September 2005 Revised 11 May 2006 Accepted 18 May 2006 Recommended by Kostas Berberidis Several linear and nonlinear detection algorithms that are based on spectral matched subspace filters are compared. Nonlinear kernel versions of these spectral matched detectors are also given and their performance is compared with linear versions. Several well-known matched detectors such as matched subspace detector orthogonal subspace detector spectral matched filter and adaptive subspace detector are extended to their corresponding kernel versions by using the idea of kernel-based learning theory. In kernel-based detection algorithms the data is assumed to be implicitly mapped into a high-dimensional kernel feature space by a nonlinear mapping which is associated with a kernel function. The expression for each detection algorithm is then derived in the feature space which is kernelized in terms of the kernel functions in order to avoid explicit computation in the high-dimensional feature space. Experimental results based on simulated toy examples and real hyperspectral imagery show that the kernel versions of these detectors outperform the conventional linear detectors. Copyright 2007 H. Kwon and N. M. Nasrabadi. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited. 1. INTRODUCTION Detecting signals of interest particularly with wide signal variability in noisy environments has long been a challenging issue in various fields of signal .