ATT-CR: Adaptive Triangular Transformer for Cloud Removal
Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images. Existing transformer-based methods utilizing self-attention have shown impressive results by effectively modeling long-range dependencies in cloudy images. However, they suffer from the follo...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/11119343/ |
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| _version_ | 1849223120426106880 |
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| author | Yang Wu Ye Deng Pengna Li Wenli Huang Kangyi Wu Xiaomeng Xin Jinjun Wang |
| author_facet | Yang Wu Ye Deng Pengna Li Wenli Huang Kangyi Wu Xiaomeng Xin Jinjun Wang |
| author_sort | Yang Wu |
| collection | DOAJ |
| description | Cloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images. Existing transformer-based methods utilizing self-attention have shown impressive results by effectively modeling long-range dependencies in cloudy images. However, they suffer from the following issues: 1) the high computational complexity of self-attention limits scalability; 2) treating both cloudy and clean pixels as valid within the attention computation brings disturbances in subsequent layers, leading to suboptimal performance. To address these challenges, we propose the adaptive triangular transformer for cloud removal (ATT-CR), a model that effectively reduces computational costs and mitigates interference from cloudy pixels. Specifically, it consists of two core components: Triangular attention (TAN) and feature selected gating module (FSGM). TAN employs lower and upper triangular matrices to approximate softmax attention with <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N)$</tex-math></inline-formula> computational complexity, significantly reducing the computational costs. The FSGM, on the other hand, integrates with TAN to adaptively distinguish between cloudy and clean features, which minimizes the introduction of invalid information into subsequent layers. Extensive experiments on cloud removal benchmarks demonstrate that ATT-CR delivers superior performance compared to existing methods. |
| format | Article |
| id | doaj-art-453f0a2964e1467daeb224efc84e17b4 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-453f0a2964e1467daeb224efc84e17b42025-08-25T23:00:17ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118205952061010.1109/JSTARS.2025.359685611119343ATT-CR: Adaptive Triangular Transformer for Cloud RemovalYang Wu0https://orcid.org/0009-0006-3944-6415Ye Deng1https://orcid.org/0000-0003-4616-3318Pengna Li2https://orcid.org/0000-0002-8477-8340Wenli Huang3https://orcid.org/0000-0002-8636-044XKangyi Wu4Xiaomeng Xin5https://orcid.org/0009-0006-6307-1465Jinjun Wang6https://orcid.org/0000-0002-9434-0617Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, ChinaEngineering Research Center of Intelligent Finance, Ministry of Education, School of Computing and Artificial Intelligence, Southwestern University of Finance and Economics, Chengdu, ChinaInstitute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, ChinaNingbo University of Technology, Ningbo, ChinaInstitute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, ChinaInstitute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, ChinaInstitute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, ChinaCloud removal aims to accurately reconstruct the ground objects obscured by clouds in remote sensing images. Existing transformer-based methods utilizing self-attention have shown impressive results by effectively modeling long-range dependencies in cloudy images. However, they suffer from the following issues: 1) the high computational complexity of self-attention limits scalability; 2) treating both cloudy and clean pixels as valid within the attention computation brings disturbances in subsequent layers, leading to suboptimal performance. To address these challenges, we propose the adaptive triangular transformer for cloud removal (ATT-CR), a model that effectively reduces computational costs and mitigates interference from cloudy pixels. Specifically, it consists of two core components: Triangular attention (TAN) and feature selected gating module (FSGM). TAN employs lower and upper triangular matrices to approximate softmax attention with <inline-formula><tex-math notation="LaTeX">$\mathcal {O}(N)$</tex-math></inline-formula> computational complexity, significantly reducing the computational costs. The FSGM, on the other hand, integrates with TAN to adaptively distinguish between cloudy and clean features, which minimizes the introduction of invalid information into subsequent layers. Extensive experiments on cloud removal benchmarks demonstrate that ATT-CR delivers superior performance compared to existing methods.https://ieeexplore.ieee.org/document/11119343/Adaptive feature selectioncloud removalimage reconstructionremote sensing imagestriangular attention (TAN) |
| spellingShingle | Yang Wu Ye Deng Pengna Li Wenli Huang Kangyi Wu Xiaomeng Xin Jinjun Wang ATT-CR: Adaptive Triangular Transformer for Cloud Removal IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Adaptive feature selection cloud removal image reconstruction remote sensing images triangular attention (TAN) |
| title | ATT-CR: Adaptive Triangular Transformer for Cloud Removal |
| title_full | ATT-CR: Adaptive Triangular Transformer for Cloud Removal |
| title_fullStr | ATT-CR: Adaptive Triangular Transformer for Cloud Removal |
| title_full_unstemmed | ATT-CR: Adaptive Triangular Transformer for Cloud Removal |
| title_short | ATT-CR: Adaptive Triangular Transformer for Cloud Removal |
| title_sort | att cr adaptive triangular transformer for cloud removal |
| topic | Adaptive feature selection cloud removal image reconstruction remote sensing images triangular attention (TAN) |
| url | https://ieeexplore.ieee.org/document/11119343/ |
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