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Cone-based Joint Sparse Modelling for Hyperspectral Image Classification

Wang, Z; Zhu, R; Fukui, K; Xue, J-H; (2018) Cone-based Joint Sparse Modelling for Hyperspectral Image Classification. Signal Processing , 144 pp. 417-429. 10.1016/j.sigpro.2017.11.001. Green open access

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Abstract

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.

Type: Article
Title: Cone-based Joint Sparse Modelling for Hyperspectral Image Classification
Open access status: An open access version is available from UCL Discovery
DOI: 10.1016/j.sigpro.2017.11.001
Publisher version: https://doi.org/10.1016/j.sigpro.2017.11.001
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Hyperspectral image classification, Joint sparse model, Simultaneous orthogonal matching pursuit, Conenon-negativity
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10040798
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