DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution
Transformer-based methods have demonstrated impressive performance in image super-resolution tasks. However, when applied to large-scale Earth observation images, the existing transformers encounter two significant challenges: (1) insufficient consideration of spatial correlation between adjacent gr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/22/4251 |
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| _version_ | 1850226563960799232 |
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| author | Yujie Mao Guojin He Guizhou Wang Ranyu Yin Yan Peng Bin Guan |
| author_facet | Yujie Mao Guojin He Guizhou Wang Ranyu Yin Yan Peng Bin Guan |
| author_sort | Yujie Mao |
| collection | DOAJ |
| description | Transformer-based methods have demonstrated impressive performance in image super-resolution tasks. However, when applied to large-scale Earth observation images, the existing transformers encounter two significant challenges: (1) insufficient consideration of spatial correlation between adjacent ground objects; and (2) performance bottlenecks due to the underutilization of the upsample module. To address these issues, we propose a novel distance-enhanced strip attention transformer (DESAT). The DESAT integrates distance priors, easily obtainable from remote sensing images, into the strip window self-attention mechanism to capture spatial correlations more effectively. To further enhance the transfer of deep features into high-resolution outputs, we designed an attention-enhanced upsample block, which combines the pixel shuffle layer with an attention-based upsample branch implemented through the overlapping window self-attention mechanism. Additionally, to better simulate real-world scenarios, we constructed a new cross-sensor super-resolution dataset using Gaofen-6 satellite imagery. Extensive experiments on both simulated and real-world remote sensing datasets demonstrate that the DESAT outperforms state-of-the-art models by up to 1.17 dB along with superior qualitative results. Furthermore, the DESAT achieves more competitive performance in real-world tasks, effectively balancing spatial detail reconstruction and spectral transform, making it highly suitable for practical remote sensing super-resolution applications. |
| format | Article |
| id | doaj-art-eb27d84f61c14123be732fe76e46eed2 |
| institution | OA Journals |
| issn | 2072-4292 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-eb27d84f61c14123be732fe76e46eed22025-08-20T02:05:02ZengMDPI AGRemote Sensing2072-42922024-11-011622425110.3390/rs16224251DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-ResolutionYujie Mao0Guojin He1Guizhou Wang2Ranyu Yin3Yan Peng4Bin Guan5Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaAerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, ChinaSchool of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, ChinaTransformer-based methods have demonstrated impressive performance in image super-resolution tasks. However, when applied to large-scale Earth observation images, the existing transformers encounter two significant challenges: (1) insufficient consideration of spatial correlation between adjacent ground objects; and (2) performance bottlenecks due to the underutilization of the upsample module. To address these issues, we propose a novel distance-enhanced strip attention transformer (DESAT). The DESAT integrates distance priors, easily obtainable from remote sensing images, into the strip window self-attention mechanism to capture spatial correlations more effectively. To further enhance the transfer of deep features into high-resolution outputs, we designed an attention-enhanced upsample block, which combines the pixel shuffle layer with an attention-based upsample branch implemented through the overlapping window self-attention mechanism. Additionally, to better simulate real-world scenarios, we constructed a new cross-sensor super-resolution dataset using Gaofen-6 satellite imagery. Extensive experiments on both simulated and real-world remote sensing datasets demonstrate that the DESAT outperforms state-of-the-art models by up to 1.17 dB along with superior qualitative results. Furthermore, the DESAT achieves more competitive performance in real-world tasks, effectively balancing spatial detail reconstruction and spectral transform, making it highly suitable for practical remote sensing super-resolution applications.https://www.mdpi.com/2072-4292/16/22/4251remote sensingimage super-resolutiondeep learningtransformerself-attentionGaofen-6 satellite |
| spellingShingle | Yujie Mao Guojin He Guizhou Wang Ranyu Yin Yan Peng Bin Guan DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution Remote Sensing remote sensing image super-resolution deep learning transformer self-attention Gaofen-6 satellite |
| title | DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution |
| title_full | DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution |
| title_fullStr | DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution |
| title_full_unstemmed | DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution |
| title_short | DESAT: A Distance-Enhanced Strip Attention Transformer for Remote Sensing Image Super-Resolution |
| title_sort | desat a distance enhanced strip attention transformer for remote sensing image super resolution |
| topic | remote sensing image super-resolution deep learning transformer self-attention Gaofen-6 satellite |
| url | https://www.mdpi.com/2072-4292/16/22/4251 |
| work_keys_str_mv | AT yujiemao desatadistanceenhancedstripattentiontransformerforremotesensingimagesuperresolution AT guojinhe desatadistanceenhancedstripattentiontransformerforremotesensingimagesuperresolution AT guizhouwang desatadistanceenhancedstripattentiontransformerforremotesensingimagesuperresolution AT ranyuyin desatadistanceenhancedstripattentiontransformerforremotesensingimagesuperresolution AT yanpeng desatadistanceenhancedstripattentiontransformerforremotesensingimagesuperresolution AT binguan desatadistanceenhancedstripattentiontransformerforremotesensingimagesuperresolution |