期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 54, 期 3, 页码 1818-1833出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2015.2489218
关键词
Coupling; denoising; hyperspectral image (HSI); sparsity; unmixing
类别
资金
- National Natural Science Foundation of China (NSFC) [61371152, 61071172, 61374162]
- NSFC
- National Research Foundation of Korea Scientific Cooperation Program [6151101013]
- New Century Excellent Talents Award Program from the Ministry of Education of China [NCET-12-0464]
- Ministry of Education Scientific Research Foundation for the Returned Overseas
- Fundamental Research Funds for the Central Universities [3102015ZY045]
- China Scholarship Council [201506290120]
Hyperspectral image (HSI) denoising is significant for correct interpretation. In this paper, a sparse representation framework that unifies denoising and spectral unmixing in a closed-loop manner is proposed. While conventional approaches treat denoising and unmixing separately, the proposed scheme utilizes spectral information from unmixing as feedback to correct spectral distortion. Both denoising and spectral unmixing act as constraints to the others and are solved iteratively. Noise is suppressed via sparse coding, and fractional abundance in spectral unmixing is estimated using the sparsity prior of endmembers from a spectral library. The abundance of endmembers is used as a spectral regularizer for denoising based on the hypothesis that spectral signatures obtained from a denoising process result are close to those of unmixing. Unmixing restrains spectral distortion and results in better denoising, which reciprocally leads to further improvements in unmixing. The strength of our proposed method is illustrated by simulated and real HSIs with performance competitive to the state-of-the-art denoising and unmixing methods.
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