DA-Net: Dual Attention Network for Haze Removal in Remote Sensing Image
Haze removal in remote sensing images is essential for practical applications in various fields such as weather forecasting, monitoring, mineral exploration and disaster management. The previous deep learning models make use of large convolutional kernel and attention mechanisms for efficient dehazi...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10679105/ |
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| author | Namwon Kim Il-Seok Choi Seong-Soo Han Chang-Sung Jeong |
| author_facet | Namwon Kim Il-Seok Choi Seong-Soo Han Chang-Sung Jeong |
| author_sort | Namwon Kim |
| collection | DOAJ |
| description | Haze removal in remote sensing images is essential for practical applications in various fields such as weather forecasting, monitoring, mineral exploration and disaster management. The previous deep learning models make use of large convolutional kernel and attention mechanisms for efficient dehazing. However, it has drawbacks such as the loss of image details and low performance. In this paper, we shall present a new dual attention network, called DA-Net, for dehazing remote sensing images which achieves better dehazing performance while reducing model complexity sharply by exploiting a novel dual attention block where two modules, channel-spatial attention and parallel attention are serially connected. We propose a new architecture for parallel attention which achieves better dehazing performance by concatenating three different attention mechanisms in parallel: global channel attention, local channel attention and spatial attention. Moreover, we shall show that the concatenation of channel-spatial attention to parallel attention module enables detecting haze component information more accurately while reducing the model complexity proportional to the number of parameters by combining the channel and spatial information generated respectively from two different channel and spatial branches. Our experimental results show that DA-Net achieves much better performance for both synthetic and real image data sets compared to the other dehazing models in terms of quantitative and qualitative evaluations. |
| format | Article |
| id | doaj-art-5a6dd40ec69c45fa8a28fc565a5d01a5 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5a6dd40ec69c45fa8a28fc565a5d01a52025-08-20T02:48:49ZengIEEEIEEE Access2169-35362024-01-011213629713631210.1109/ACCESS.2024.345958810679105DA-Net: Dual Attention Network for Haze Removal in Remote Sensing ImageNamwon Kim0https://orcid.org/0000-0002-1865-5537Il-Seok Choi1Seong-Soo Han2Chang-Sung Jeong3Department of Electrical Engineering, Korea University, Seoul, South KoreaCTO SW Engineering Research and Development Laboratory, LG Electronics, Seoul, South KoreaDivision of Liberal Studies, Kangwon National University, Samcheok-si, South KoreaDepartment of Electrical Engineering, Korea University, Seoul, South KoreaHaze removal in remote sensing images is essential for practical applications in various fields such as weather forecasting, monitoring, mineral exploration and disaster management. The previous deep learning models make use of large convolutional kernel and attention mechanisms for efficient dehazing. However, it has drawbacks such as the loss of image details and low performance. In this paper, we shall present a new dual attention network, called DA-Net, for dehazing remote sensing images which achieves better dehazing performance while reducing model complexity sharply by exploiting a novel dual attention block where two modules, channel-spatial attention and parallel attention are serially connected. We propose a new architecture for parallel attention which achieves better dehazing performance by concatenating three different attention mechanisms in parallel: global channel attention, local channel attention and spatial attention. Moreover, we shall show that the concatenation of channel-spatial attention to parallel attention module enables detecting haze component information more accurately while reducing the model complexity proportional to the number of parameters by combining the channel and spatial information generated respectively from two different channel and spatial branches. Our experimental results show that DA-Net achieves much better performance for both synthetic and real image data sets compared to the other dehazing models in terms of quantitative and qualitative evaluations.https://ieeexplore.ieee.org/document/10679105/Dehazingremote sensing imagedeep learningattention |
| spellingShingle | Namwon Kim Il-Seok Choi Seong-Soo Han Chang-Sung Jeong DA-Net: Dual Attention Network for Haze Removal in Remote Sensing Image IEEE Access Dehazing remote sensing image deep learning attention |
| title | DA-Net: Dual Attention Network for Haze Removal in Remote Sensing Image |
| title_full | DA-Net: Dual Attention Network for Haze Removal in Remote Sensing Image |
| title_fullStr | DA-Net: Dual Attention Network for Haze Removal in Remote Sensing Image |
| title_full_unstemmed | DA-Net: Dual Attention Network for Haze Removal in Remote Sensing Image |
| title_short | DA-Net: Dual Attention Network for Haze Removal in Remote Sensing Image |
| title_sort | da net dual attention network for haze removal in remote sensing image |
| topic | Dehazing remote sensing image deep learning attention |
| url | https://ieeexplore.ieee.org/document/10679105/ |
| work_keys_str_mv | AT namwonkim danetdualattentionnetworkforhazeremovalinremotesensingimage AT ilseokchoi danetdualattentionnetworkforhazeremovalinremotesensingimage AT seongsoohan danetdualattentionnetworkforhazeremovalinremotesensingimage AT changsungjeong danetdualattentionnetworkforhazeremovalinremotesensingimage |