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

Jiaojiao Li; Yunsong Li; Xianyun Wu; Jiahui Liu; Keyan Wang; Kai Liu

doi:10.1117/12.2022505

24 September 2013 Spatial correlations constrained sparse unmixing of hyperspectral image using Adapting Markov Random Fields

Jiaojiao Li, Yunsong Li, Xianyun Wu, Jiahui Liu, Keyan Wang, Kai Liu

Author Affiliations +

Proceedings Volume 8871, Satellite Data Compression, Communications, and Processing IX; 88710E (2013) https://doi.org/10.1117/12.2022505
Event: SPIE Optical Engineering + Applications, 2013, San Diego, California, United States

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.

Citation Download Citation

Jiaojiao Li, Yunsong Li, Xianyun Wu, Jiahui Liu, Keyan Wang, and Kai Liu "Spatial correlations constrained sparse unmixing of hyperspectral image using Adapting Markov Random Fields", Proc. SPIE 8871, Satellite Data Compression, Communications, and Processing IX, 88710E (24 September 2013); https://doi.org/10.1117/12.2022505

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KEYWORDS

Hyperspectral imaging

Copper

Lithium

Signal to noise ratio

Error analysis

Image processing

Magnetorheological finishing

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