Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation
AbstractAutomatic cloud detection and classification using satellite cloud imagery have various meteorological applications such as weather forecasting and climate monitoring. Cloud pattern analysis is one of the research hotspots recently. Since satellites sense the clouds remotely from space, and different cloud types often overlap and convert into each other, there must be some fuzziness and uncertainty in satellite cloud imagery. Satellite observation is susceptible to noises, while traditional cloud classification methods are sensitive to noises and outliers; it is hard for traditional cloud classification methods to achieve reliable results. To deal with these problems, a satellite cloud classification method using adaptive fuzzy sparse representation-based classification (AFSRC) is proposed. Firstly, by defining adaptive parameters related to attenuation rate and critical membership, an improved fuzzy membership is introduced to accommodate the fuzziness and uncertainty of satellite cloud imagery; secondly, by effective combination of the improved fuzzy membership function and sparse representation-based classification (SRC), atoms in training dictionary are optimized; finally, an adaptive fuzzy sparse representation classifier for cloud classification is proposed. Experiment results on FY-2G satellite cloud image show that, the proposed method not only improves the accuracy of cloud classification, but also has strong stability and adaptability with high computational efficiency. View Full-Text
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Jin, W.; Gong, F.; Zeng, X.; Fu, R. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation. Sensors 2016, 16, 2153.
Jin W, Gong F, Zeng X, Fu R. Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation. Sensors. 2016; 16(12):2153.Chicago/Turabian Style
Jin, Wei; Gong, Fei; Zeng, Xingbin; Fu, Randi. 2016. "Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation." Sensors 16, no. 12: 2153.
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