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Matched Shrunken Cone Detector (MSCD): Bayesian Derivations and Case Studies for Hyperspectral Target Detection

Wang, Z., Zhu, R. ORCID: 0000-0002-9944-0369, Fukui, K. & Xue, J-H. (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. doi: 10.1109/TIP.2017.2740621


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.

Publication Type: Article
Additional Information: This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see
Publisher Keywords: Target detection, hyperspectral image (HSI), cone representation, non-negativity, half-Gaussian distribution, half-Laplace distribution, shrunken estimation.
Subjects: H Social Sciences > HA Statistics
Q Science > QA Mathematics
Departments: Bayes Business School > Actuarial Science & Insurance
Text - Published Version
Available under License Creative Commons: Attribution 3.0.

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