Satellite Image Inpainting With Edge-Conditional Expectation Attention
Satellite images often suffer from data loss and corruption due to various factors, including sensor malfunctions and atmospheric interference, leading to incomplete and degraded imagery. In satellite images, long-range dependencies are particularly significant due to irregular and widely distribute...
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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/10959709/ |
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| author | Dazhi Zhou Yanjun Chen Yuhong Zhang Jing Niu |
| author_facet | Dazhi Zhou Yanjun Chen Yuhong Zhang Jing Niu |
| author_sort | Dazhi Zhou |
| collection | DOAJ |
| description | Satellite images often suffer from data loss and corruption due to various factors, including sensor malfunctions and atmospheric interference, leading to incomplete and degraded imagery. In satellite images, long-range dependencies are particularly significant due to irregular and widely distributed geomorphological edges, such as rivers, mountains, and urban structures. Traditional convolutional neural network-based inpainting methods face challenges due to their fixed receptive fields and parameter sharing, limiting their ability to effectively capture long-range dependencies and differentiate between corrupted and uncorrupted areas. To address these limitations, we propose a deep learning approach based on an edge-conditional expectation attention module, which conditions the attention mechanism on edge information to enhance the model's focus on high-frequency edge details. This enables the network to capture critical structures within the image better. In addition, we apply Chebyshev’s inequality within the attention mechanism to constrain the expectation of attention outputs, reducing excessive deviations and stabilizing the reconstruction process. Experimental results demonstrate that our approach outperforms several state-of-the-art methods in restoring missing regions and reconnecting geomorphological features. |
| format | Article |
| id | doaj-art-de84d1529b2a4161ba6d6817ffa7d905 |
| institution | OA Journals |
| 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-de84d1529b2a4161ba6d6817ffa7d9052025-08-20T01:48:20ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118108301084510.1109/JSTARS.2025.355920310959709Satellite Image Inpainting With Edge-Conditional Expectation AttentionDazhi Zhou0https://orcid.org/0009-0006-6951-2145Yanjun Chen1https://orcid.org/0000-0002-9111-6222Yuhong Zhang2Jing Niu3https://orcid.org/0000-0002-3932-5860School of Mathematical Sciences, Harbin Normal University, Harbin, ChinaSchool of Mathematical Sciences, Harbin Normal University, Harbin, ChinaSchool of Geographical Sciences, Harbin Normal University, Harbin, ChinaSchool of Mathematical Sciences, Harbin Normal University, Harbin, ChinaSatellite images often suffer from data loss and corruption due to various factors, including sensor malfunctions and atmospheric interference, leading to incomplete and degraded imagery. In satellite images, long-range dependencies are particularly significant due to irregular and widely distributed geomorphological edges, such as rivers, mountains, and urban structures. Traditional convolutional neural network-based inpainting methods face challenges due to their fixed receptive fields and parameter sharing, limiting their ability to effectively capture long-range dependencies and differentiate between corrupted and uncorrupted areas. To address these limitations, we propose a deep learning approach based on an edge-conditional expectation attention module, which conditions the attention mechanism on edge information to enhance the model's focus on high-frequency edge details. This enables the network to capture critical structures within the image better. In addition, we apply Chebyshev’s inequality within the attention mechanism to constrain the expectation of attention outputs, reducing excessive deviations and stabilizing the reconstruction process. Experimental results demonstrate that our approach outperforms several state-of-the-art methods in restoring missing regions and reconnecting geomorphological features.https://ieeexplore.ieee.org/document/10959709/Chebyshev adjustmentedge extractionimage inpaintingremote sensingtransformer |
| spellingShingle | Dazhi Zhou Yanjun Chen Yuhong Zhang Jing Niu Satellite Image Inpainting With Edge-Conditional Expectation Attention IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Chebyshev adjustment edge extraction image inpainting remote sensing transformer |
| title | Satellite Image Inpainting With Edge-Conditional Expectation Attention |
| title_full | Satellite Image Inpainting With Edge-Conditional Expectation Attention |
| title_fullStr | Satellite Image Inpainting With Edge-Conditional Expectation Attention |
| title_full_unstemmed | Satellite Image Inpainting With Edge-Conditional Expectation Attention |
| title_short | Satellite Image Inpainting With Edge-Conditional Expectation Attention |
| title_sort | satellite image inpainting with edge conditional expectation attention |
| topic | Chebyshev adjustment edge extraction image inpainting remote sensing transformer |
| url | https://ieeexplore.ieee.org/document/10959709/ |
| work_keys_str_mv | AT dazhizhou satelliteimageinpaintingwithedgeconditionalexpectationattention AT yanjunchen satelliteimageinpaintingwithedgeconditionalexpectationattention AT yuhongzhang satelliteimageinpaintingwithedgeconditionalexpectationattention AT jingniu satelliteimageinpaintingwithedgeconditionalexpectationattention |