Recovering Quantitative Remote Sensing Products Contaminated by Thick Clouds and Shadows Using Multitemporal Dictionary Learning

• Published in: IEEE transactions on geoscience and remote sensing Vol. 52; no. 11; pp. 7086 - 7098
• Main Authors: Li, Xinghua, Shen, Huanfeng, Zhang, Liangpei, Zhang, Hongyan, Yuan, Qiangqiang, Yang, Gang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01-11-2014; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Bayes methods; Bayesian analysis; Clouds; Compressed sensing (CS); Contamination; Correlation; Dictionaries; dictionary learning; Land surface temperature; land surface temperature (LST); Learning; multitemporal; quantitative remote sensing (QRS) product; reflectance; Remote sensing; Shadows; thick clouds; Transforms; multitemporal; land surface temperature (LST); reflectance; Compressed sensing (CS); shadows; thick clouds; dictionary learning; quantitative remote sensing (QRS) product
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2014.2307354

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.