An Attention Encoder-Decoder Network Based on Generative Adversarial Network for Remote Sensing Image Dehazing
Remote sensing image dehazing is a difficult problem for its complex characteristics. It can be regarded as the preprocessing of high-level tasks of remote sensing images. To remove haze from the hazy remote sensing image, an encoder-decoder based on generative adversarial network is proposed. It fi...
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| Veröffentlicht in: | IEEE sensors journal Jg. 22; H. 11; S. 10890 - 10900 |
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| Hauptverfasser: | , , |
| Format: | Journal Article |
| Sprache: | Englisch |
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New York
IEEE
01.06.2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
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| ISSN: | 1530-437X, 1558-1748 |
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| Abstract | Remote sensing image dehazing is a difficult problem for its complex characteristics. It can be regarded as the preprocessing of high-level tasks of remote sensing images. To remove haze from the hazy remote sensing image, an encoder-decoder based on generative adversarial network is proposed. It first learns the low-frequency information of the image, and then learns the high-frequency information of the image. The skip connection is also added in the network to avoid losing information. To further improve the ability of learning more useful information, a multi-scale attention module is proposed. Meanwhile, a CBlock module is also designed to extract more feature information. It can capture different size of receptive fields. In order to reduce the computational pressure of the network, a distillation module is used in the network. Inspired by multi-scale network, an enhance module is designed and introduced it in the end of the network to further improve the dehazing ability of the network by integrating context information on multi-scale. We compared with five methods and our proposed method on RICE dataset. Experimental results show that our method achieves the best effect, both qualitatively and quantitatively. |
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| AbstractList | Remote sensing image dehazing is a difficult problem for its complex characteristics. It can be regarded as the preprocessing of high-level tasks of remote sensing images. To remove haze from the hazy remote sensing image, an encoder-decoder based on generative adversarial network is proposed. It first learns the low-frequency information of the image, and then learns the high-frequency information of the image. The skip connection is also added in the network to avoid losing information. To further improve the ability of learning more useful information, a multi-scale attention module is proposed. Meanwhile, a CBlock module is also designed to extract more feature information. It can capture different size of receptive fields. In order to reduce the computational pressure of the network, a distillation module is used in the network. Inspired by multi-scale network, an enhance module is designed and introduced it in the end of the network to further improve the dehazing ability of the network by integrating context information on multi-scale. We compared with five methods and our proposed method on RICE dataset. Experimental results show that our method achieves the best effect, both qualitatively and quantitatively. |
| Author | Zhang, Yupeng Zhao, Liquan Cui, Ying |
| Author_xml | – sequence: 1 givenname: Liquan orcidid: 0000-0002-9499-1911 surname: Zhao fullname: Zhao, Liquan email: zhao_liquan@163.com organization: Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China – sequence: 2 givenname: Yupeng surname: Zhang fullname: Zhang, Yupeng email: zhang_yupeng2021@163.com organization: Key Laboratory of Modern Power System Simulation and Control and Renewable Energy Technology, Ministry of Education, Northeast Electric Power University, Jilin, China – sequence: 3 givenname: Ying surname: Cui fullname: Cui, Ying email: cuiying202107@163.com organization: Guangdong Electric Power Corporation Zhuhai Power Supply Bureau, Zhuhai, China |
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| SubjectTerms | Atmospheric modeling Coders Distillation encoder-decoder Encoders-Decoders Feature extraction generative adversarial network Generative adversarial networks Haze Learning systems Modules multi-scale attention module Remote sensing Remote sensing image dehazing Scattering Task complexity Training |
| Title | An Attention Encoder-Decoder Network Based on Generative Adversarial Network for Remote Sensing Image Dehazing |
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