期刊
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
卷 52, 期 11, 页码 7086-7098出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2014.2307354
关键词
Compressed sensing (CS); dictionary learning; land surface temperature (LST); multitemporal; quantitative remote sensing (QRS) product; reflectance; shadows; thick clouds
类别
资金
- National Natural Science Foundation of China [41271376]
- National Major Basic Research Development Program of P.R. China (973 Program) [2011CB707105]
- Program for Changjiang Scholars and Innovative Research Team in University [IRT1278]
- Wuhan Municipal Science and Technology Bureau [2013072304010825]
With regard to quantitative remote sensing products in the visible and infrared ranges, thick clouds and accompanying shadows are an inevitable source of noise. Due to the absence of adequate supporting information from the data themselves, it is a formidable challenge to accurately restore the surficial information underlying large-scale clouds. In this paper, dictionary learning is expanded into the multitemporal recovery of quantitative data contaminated by thick clouds and shadows. This paper proposes two multitemporal dictionary learning algorithms, expanding on their KSVD and Bayesian counterparts. In order to make better use of the temporal correlations, the expanded KSVD algorithm seeks an optimized temporal path, and the expanded Bayesian method adaptively weights the temporal correlations. In the experiments, the proposed algorithms are applied to a reflectance product and a land surface temperature product, and the respective advantages of the two algorithms are investigated. The results show that, from both the qualitative visual effect and the quantitative objective evaluation, the proposed methods are effective.
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