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Proceedings Paper

Spatial correlations constrained sparse unmixing of hyperspectral image using Adapting Markov Random Fields
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Paper Abstract

Spectral unmixing is an important research hotspot for remote sensing hyperspectral image applications. The unmixing process is comprised of the extraction of spectrally pure signatures (also called endmembers) and the determination of the abundance fractions of endmembers. Due to the inconspicuous signatures of pure spectra and the challenge of inadequate spatial resolution, sparse regression (SR) techniques are adopted in solving the linear spectral unmixing problem. However, the spatial information has not been fully utilized by state-of-art SR-based solutions. In this paper, we propose a new unmixing algorithm which involves in more suitable spatial correlations on sparse unmixing formulation for hyperspectral image. Our algorithm integrates the spectral and spatial information using Adapting Markov Random Fields (AMRF) which is introduced to exploit the spatial-contextual information. Compared with other SR-based linear unmixing methods, the experimental results show that the method proposed in this paper not only improves the characterization of mixed pixels but also obtains better accuracy in hyperspectral image unmixing.

Paper Details

Date Published: 24 September 2013
PDF: 10 pages
Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 88710E (24 September 2013); doi: 10.1117/12.2022505
Show Author Affiliations
Jiaojiao Li, Xidian Univ. (China)
Yunsong Li, Xidian Univ. (China)
Xianyun Wu, Xidian Univ. (China)
Jiahui Liu, Xidian Univ. (China)
Keyan Wang, Xidian Univ. (China)
Kai Liu, Xidian Univ. (China)


Published in SPIE Proceedings Vol. 8871:
Satellite Data Compression, Communications, and Processing IX
Bormin Huang; Antonio J. Plaza; Chein-I Chang, Editor(s)

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