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Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation
Automatic 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 differen...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191133/ https://www.ncbi.nlm.nih.gov/pubmed/27999261 http://dx.doi.org/10.3390/s16122153 |
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author | Jin, Wei Gong, Fei Zeng, Xingbin Fu, Randi |
author_facet | Jin, Wei Gong, Fei Zeng, Xingbin Fu, Randi |
author_sort | Jin, Wei |
collection | PubMed |
description | Automatic 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. |
format | Online Article Text |
id | pubmed-5191133 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-51911332017-01-03 Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation Jin, Wei Gong, Fei Zeng, Xingbin Fu, Randi Sensors (Basel) Article Automatic 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. MDPI 2016-12-16 /pmc/articles/PMC5191133/ /pubmed/27999261 http://dx.doi.org/10.3390/s16122153 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Jin, Wei Gong, Fei Zeng, Xingbin Fu, Randi Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation |
title | Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation |
title_full | Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation |
title_fullStr | Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation |
title_full_unstemmed | Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation |
title_short | Classification of Clouds in Satellite Imagery Using Adaptive Fuzzy Sparse Representation |
title_sort | classification of clouds in satellite imagery using adaptive fuzzy sparse representation |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5191133/ https://www.ncbi.nlm.nih.gov/pubmed/27999261 http://dx.doi.org/10.3390/s16122153 |
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