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Cone-based joint sparse modelling for hyperspectral image classification

Wang, Z., Zhu, R. ORCID: 0000-0002-9944-0369, Fukui, K. & Xue, J-H. (2018). Cone-based joint sparse modelling for hyperspectral image classification. Signal Processing, 144, pp. 417-429. doi: 10.1016/j.sigpro.2017.11.001


Joint sparse model (JSM) is being extensively investigated on hyperspectral images (HSIs) and has achieved promising performance for classification. In JSM, it is assumed that neighbouring hyperspectral pixels can share sparse representations. However, the coefficients of the endmembers used to reconstruct a test HSI pixel is desirable to be non-negative for the sake of physical interpretation. Hence in this paper, we introduce the non-negativity constraint into JSM. The non-negativity constraint implies a cone-shaped space instead of the infinite sample space for pixel representation. This leads us to propose a new model called cone-based joint sparse model (C-JSM), to install the non-negativity on top of the sparse and joint modelling. To solve the C-JSM problem, we also propose a new algorithm through introducing the non-negativity constraint into the simultaneous orthogonal matching pursuit (SOMP) algorithm. The new algorithm is called non-negative simultaneous orthogonal matching pursuit (NN-SOMP). Experiments and investigations show that the proposed C-JSM can produce a more stable, sparse representation and a superior classification than other methods which only ensure the sparsity, non-negativity or spatial coherence.

Publication Type: Article
Additional Information: © 2017 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
Publisher Keywords: Hyperspectral image classification, Joint sparse model, Simultaneous orthogonal matching pursuit, Cone, non-negativity
Subjects: H Social Sciences > HA Statistics
Departments: Bayes Business School > Actuarial Science & Insurance
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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