Attentive generative adversarial network for removing thin cloud from a single remote sensing image
Abstract Land‐surface observation is easily affected by the light transmission and scattering of semi‐transparent clouds, high or low, resulting in blurring and reduced contrast of ground objects. To improve the visual appearance of remote sensing images, the authors present a deep learning method f...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-03-01
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| Series: | IET Image Processing |
| Subjects: | |
| Online Access: | https://doi.org/10.1049/ipr2.12067 |
| _version_ | 1797986137360302080 |
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| author | Hui Chen Rong Chen Nannan Li |
| author_facet | Hui Chen Rong Chen Nannan Li |
| author_sort | Hui Chen |
| collection | DOAJ |
| description | Abstract Land‐surface observation is easily affected by the light transmission and scattering of semi‐transparent clouds, high or low, resulting in blurring and reduced contrast of ground objects. To improve the visual appearance of remote sensing images, the authors present a deep learning method for thin cloud removal using a new attentive generative adversarial network without prior knowledge or assumptions, which copes with thin clouds that are unevenly distributed on different images and learns the attention map with weighted information about spatial features. Such a spatial attention model can endow each pixel with the global spatial context information. Consequently, the generative network focuses on the thin cloud regions to generate better local image restoration, and the discriminative network can evaluate the local consistency of the repaired regions. The experimental results show that this method is superior to state‐of‐the‐art methods in recovering detailed texture information. |
| first_indexed | 2024-04-11T07:29:16Z |
| format | Article |
| id | doaj.art-f48ba29866414f67a031304ac39c17ec |
| institution | Directory Open Access Journal |
| issn | 1751-9659 1751-9667 |
| language | English |
| last_indexed | 2024-04-11T07:29:16Z |
| publishDate | 2021-03-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Image Processing |
| spelling | doaj.art-f48ba29866414f67a031304ac39c17ec2022-12-22T04:36:59ZengWileyIET Image Processing1751-96591751-96672021-03-0115485686710.1049/ipr2.12067Attentive generative adversarial network for removing thin cloud from a single remote sensing imageHui Chen0Rong Chen1Nannan Li2The College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaThe College of Information Science and Technology Dalian Maritime University Dalian ChinaAbstract Land‐surface observation is easily affected by the light transmission and scattering of semi‐transparent clouds, high or low, resulting in blurring and reduced contrast of ground objects. To improve the visual appearance of remote sensing images, the authors present a deep learning method for thin cloud removal using a new attentive generative adversarial network without prior knowledge or assumptions, which copes with thin clouds that are unevenly distributed on different images and learns the attention map with weighted information about spatial features. Such a spatial attention model can endow each pixel with the global spatial context information. Consequently, the generative network focuses on the thin cloud regions to generate better local image restoration, and the discriminative network can evaluate the local consistency of the repaired regions. The experimental results show that this method is superior to state‐of‐the‐art methods in recovering detailed texture information.https://doi.org/10.1049/ipr2.12067Cloud physicsInstrumentation and techniques for geophysical, hydrospheric and lower atmosphere researchOptical, image and video signal processingComputer vision and image processing techniques |
| spellingShingle | Hui Chen Rong Chen Nannan Li Attentive generative adversarial network for removing thin cloud from a single remote sensing image IET Image Processing Cloud physics Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optical, image and video signal processing Computer vision and image processing techniques |
| title | Attentive generative adversarial network for removing thin cloud from a single remote sensing image |
| title_full | Attentive generative adversarial network for removing thin cloud from a single remote sensing image |
| title_fullStr | Attentive generative adversarial network for removing thin cloud from a single remote sensing image |
| title_full_unstemmed | Attentive generative adversarial network for removing thin cloud from a single remote sensing image |
| title_short | Attentive generative adversarial network for removing thin cloud from a single remote sensing image |
| title_sort | attentive generative adversarial network for removing thin cloud from a single remote sensing image |
| topic | Cloud physics Instrumentation and techniques for geophysical, hydrospheric and lower atmosphere research Optical, image and video signal processing Computer vision and image processing techniques |
| url | https://doi.org/10.1049/ipr2.12067 |
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