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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52615

Title: A linearly constrained signal subspace projection approach developed to detect targets in hyperspectral images
Authors: Zay-Shing Tang
Lena Chang
Hsien-Sen Hung
Contributors: 國立臺灣海洋大學:電機工程學系
Keywords: hyperspectral image,
target detection
optimal filter
multiple constraints
signal subspace projection (SSP)
Date: 2014-04
Issue Date: 2019-12-02T06:59:18Z
Publisher: Journal of Marine Science and Technology
Abstract: ABSTRACT:Hyperspectral images have been widely used for target detection. In general, target signatures should be known a priori
for filter-based detection methods. However, the uncertainty
of target signatures caused by the influence of atmospheric
interference or other random noise degrades the detection performance. Therefore, developing a robust detection method is
crucial in hyperspectral image analysis. In this study, a linearly constrained signal subspace projection approach for target
detection is proposed. Instead of using a single constraint on
target detection, an optimal filter with multiple constraints is
designed using signal subspace projection (SSP). The SSP approach fully exploits the orthogonal property of two orthogonal
subspaces; one denotes a signal subspace that contains desired
targets and undesired interference, and the other denotes a
noise subspace, which is orthogonal to signal subspace. By
projecting the weights of the detection filter on the signal subspace, the proposed SSP reduces estimation errors in target
signatures and alleviates the performance degradation caused
by the uncertainty of target signatures. The SSP approach can
detect desired targets, suppress undesired targets, and minimize interference effects. In this paper, three methods are provided for selecting multiple constraints of the desired target:
K-means, principal eigenvectors, and endmember extraction
techniques. The simulation results show that the proposed
SSP with multiple constraints on the desired target selected
using K-means has superior detection performance. Further
Relation: 23(2) pp 191-201
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/52615
Appears in Collections:[電機工程學系] 期刊論文

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