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Proceedings Paper

A multiple constrained signal subspace projection for target detection in hyperspectral images
Author(s): Lena Chang; Yen-Ting Wu; Zay-Shing Tang; Yang-Lang Chang
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Paper Abstract

In the study, we develop a multiple constrained signal subspace projection (SSP) approach to target detection. Instead of using single constraint on target detection, we design an optimal filter with multiple constraints on desired targets by using SSP. The proposed SSP approach fully exploits the orthogonal property of two orthogonal subspaces: one denoted signal subspace containing desired and undesired/background targets; the other denoted noise subspace, which is orthogonal to signal subspace. By projecting the weights of the detection filter on the signal subspace, the proposed SSP can reduces some estimation errors in target signatures and alleviate the performance degradation caused by uncertainty of target signature. The SSP approach can detect desired targets, suppress undesired targets and minimize the interference effects. In experiments, we provide three methods in selecting multiple constraints of the desired target: Kmeans, principal eigenvectors and endmenber extracting techniques. Simulation results show that the proposed SSP with multiple constraints selected by K-means has better detection performance. Furthermore, the proposed SSP with multiple constraints is a robust detection approach which could overcome the uncertainty of desired target signature in real image data.

Paper Details

Date Published: 21 May 2015
PDF: 8 pages
Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010I (21 May 2015); doi: 10.1117/12.2180419
Show Author Affiliations
Lena Chang, National Taiwan Ocean Univ. (Taiwan)
Yen-Ting Wu, National Taiwan Ocean Univ. (Taiwan)
Zay-Shing Tang, National Taiwan Ocean Univ. (Taiwan)
Yang-Lang Chang, National Taipei Univ. of Technology (Taiwan)

Published in SPIE Proceedings Vol. 9501:
Satellite Data Compression, Communications, and Processing XI
Bormin Huang; Chein-I Chang; Chulhee Lee; Yunsong Li; Qian Du, Editor(s)

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