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Satellite cloud image segmentation based on lightweight convolutional neural network
More than 50% of the images captured by optical satellites are covered by clouds, which reduces the available information in the images and seriously affects the subsequent applications of satellite images. Therefore, the identification and segmentation of cloud regions come to be one of the most im...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901801/ https://www.ncbi.nlm.nih.gov/pubmed/36745635 http://dx.doi.org/10.1371/journal.pone.0280408 |
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author | Li, Xi Chen, Shilan Wu, Jin Li, Jun Wang, Ting Tang, Junquan Hu, Tongyi Wu, Wenzhu |
author_facet | Li, Xi Chen, Shilan Wu, Jin Li, Jun Wang, Ting Tang, Junquan Hu, Tongyi Wu, Wenzhu |
author_sort | Li, Xi |
collection | PubMed |
description | More than 50% of the images captured by optical satellites are covered by clouds, which reduces the available information in the images and seriously affects the subsequent applications of satellite images. Therefore, the identification and segmentation of cloud regions come to be one of the most important problems in current satellite image processing. Due to the complexity and variability of satellite images, especially when the ground is covered with snow, the boundary information of cloud regions is difficult to be accurately identified. The fast and accurate segmentation of cloud regions is a difficult point in the current research. We propose a lightweight convolutional neural network. Firstly, channel attention is used to optimize the effective information in the feature maps as a way to improve the network’s ability to extract semantic information at each scale. Then, we fuse high and low-dimensional feature maps to enhance the network’s ability to obtain small-scale semantic information. In addition, the feature aggregation module automatically adjusts the input multi-level feature weights to highlight the details of different features. Finally, we design the fully connected conditional random field to solve the problem that some noise in the input image and local minima during training is passed to the output layer resulting in the loss of edge features. Experimental results show that the proposed method achieves 0.9695 and 0.8218 for overall accuracy and recall, respectively, which has higher segmentation accuracy with the shortest time consumption compared with other state-of-the-art methods. |
format | Online Article Text |
id | pubmed-9901801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-99018012023-02-07 Satellite cloud image segmentation based on lightweight convolutional neural network Li, Xi Chen, Shilan Wu, Jin Li, Jun Wang, Ting Tang, Junquan Hu, Tongyi Wu, Wenzhu PLoS One Research Article More than 50% of the images captured by optical satellites are covered by clouds, which reduces the available information in the images and seriously affects the subsequent applications of satellite images. Therefore, the identification and segmentation of cloud regions come to be one of the most important problems in current satellite image processing. Due to the complexity and variability of satellite images, especially when the ground is covered with snow, the boundary information of cloud regions is difficult to be accurately identified. The fast and accurate segmentation of cloud regions is a difficult point in the current research. We propose a lightweight convolutional neural network. Firstly, channel attention is used to optimize the effective information in the feature maps as a way to improve the network’s ability to extract semantic information at each scale. Then, we fuse high and low-dimensional feature maps to enhance the network’s ability to obtain small-scale semantic information. In addition, the feature aggregation module automatically adjusts the input multi-level feature weights to highlight the details of different features. Finally, we design the fully connected conditional random field to solve the problem that some noise in the input image and local minima during training is passed to the output layer resulting in the loss of edge features. Experimental results show that the proposed method achieves 0.9695 and 0.8218 for overall accuracy and recall, respectively, which has higher segmentation accuracy with the shortest time consumption compared with other state-of-the-art methods. Public Library of Science 2023-02-06 /pmc/articles/PMC9901801/ /pubmed/36745635 http://dx.doi.org/10.1371/journal.pone.0280408 Text en © 2023 Li et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Li, Xi Chen, Shilan Wu, Jin Li, Jun Wang, Ting Tang, Junquan Hu, Tongyi Wu, Wenzhu Satellite cloud image segmentation based on lightweight convolutional neural network |
title | Satellite cloud image segmentation based on lightweight convolutional neural network |
title_full | Satellite cloud image segmentation based on lightweight convolutional neural network |
title_fullStr | Satellite cloud image segmentation based on lightweight convolutional neural network |
title_full_unstemmed | Satellite cloud image segmentation based on lightweight convolutional neural network |
title_short | Satellite cloud image segmentation based on lightweight convolutional neural network |
title_sort | satellite cloud image segmentation based on lightweight convolutional neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901801/ https://www.ncbi.nlm.nih.gov/pubmed/36745635 http://dx.doi.org/10.1371/journal.pone.0280408 |
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