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Matched shrunken cone detector (MSCD) : Bayesian derivations and case studies for hyperspectral target detection

Wang, Ziyu, Zhu, Rui, Fukui, Kazuhiro, Xue, Jing-Hao (2017) Matched shrunken cone detector (MSCD) : Bayesian derivations and case studies for hyperspectral target detection. IEEE Transactions on Image Processing, 26 (11). pp. 5447-5461. ISSN 1057-7149. (doi:10.1109/TIP.2017.2740621) (KAR id:63942)

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Official URL
http://dx.doi.org/10.1109/TIP.2017.2740621

Abstract

Hyperspectral images (HSIs) possess non-negative properties for both hyperspectral signatures and abundance coefficients, which can be naturally modeled using cone-based representation. However, in hyperspectral target detection, cone-based methods are barely studied. In this paper, we propose a new regularized cone-based representation approach to hyperspectral target detection, as well as its two working models by incorporating into the cone representation l2-norm and l1-norm regularizations, respectively. We call the new approach the matched shrunken cone detector (MSCD). Also important, we provide principled derivations of the proposed MSCD from the Bayesian perspective: we show that MSCD can be derived by assuming a multivariate half-Gaussian distribution or a multivariate half-Laplace distribution as the prior distribution of the coefficients of the models. In the experimental studies, we compare the proposed MSCD with the subspace methods and the sparse representation-based methods for HSI target detection. Two real hyperspectral data sets are used for evaluating the detection performances on sub-pixel targets and full-pixel targets, respectively. Results show that the proposed MSCD can outperform other methods in both cases, demonstrating the competitiveness of the regularized cone-based representation.

Item Type: Article
DOI/Identification number: 10.1109/TIP.2017.2740621
Divisions: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Mathematics, Statistics and Actuarial Science
Depositing User: R. Zhu
Date Deposited: 10 Oct 2017 20:54 UTC
Last Modified: 16 Feb 2021 13:49 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/63942 (The current URI for this page, for reference purposes)
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