STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
Super-resolution (SR) of remote sensing images is essential to compensate for missing information in the original high-resolution (HR) images. Single-image super-resolution (SISR) technique aims to recover high-resolution images from low-resolution (LR) images. However, traditional SISR methods ofte...
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2024-12-01
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| author | Wei Huo Xiaodan Zhang Shaojie You Yongkun Zhang Qiyuan Zhang Naihao Hu |
| author_facet | Wei Huo Xiaodan Zhang Shaojie You Yongkun Zhang Qiyuan Zhang Naihao Hu |
| author_sort | Wei Huo |
| collection | DOAJ |
| description | Super-resolution (SR) of remote sensing images is essential to compensate for missing information in the original high-resolution (HR) images. Single-image super-resolution (SISR) technique aims to recover high-resolution images from low-resolution (LR) images. However, traditional SISR methods often result in blurred and unclear images due to the loss of high-frequency details in LR images at high magnifications. In this paper, a super-segmental reconstruction model STGAN for remote sensing images is proposed, which fuses the Generative Adversarial Networks (GANs) and self-attention mechanism based on the Reference Super Resolution method (RefSR). The core module of the model consists of multiple CNN-Swin Transformer blocks (MCST), each of which consists of a CNN layer and a specific modified Swin Transformer, constituting the feature extraction channel. In image hypersegmentation reconstruction, the optimized and improved correlation attention block (RAM-V) uses feature maps and gradient maps to improve the robustness of the model under different scenarios (such as land cover change). The experimental results show that the STGAN model proposed in this paper exhibits the best image data perception quality results with the best performance of LPIPS and PI metrics in the test set under RRSSRD public datasets. In the experimental test set, the PSNR reaches 31.4151, the SSIM is 0.8408, and the performance on the RMSE and SAM metrics is excellent, which demonstrate the model’s superior image reconstruction details in super-resolution reconstruction and highlighting the great potential of RefSR’s application to the task of super-scalar processing of remotely sensed images. |
| format | Article |
| id | doaj-art-16138d4158c34ca6bb2b69c9c2d1720b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-16138d4158c34ca6bb2b69c9c2d1720b2025-01-10T13:15:05ZengMDPI AGApplied Sciences2076-34172024-12-0115130510.3390/app15010305STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution ReconstructionWei Huo0Xiaodan Zhang1Shaojie You2Yongkun Zhang3Qiyuan Zhang4Naihao Hu5School of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaSchool of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaSchool of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaSchool of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaSchool of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaSchool of Computer Technology and Applications, Qinghai University, Xining 810016, ChinaSuper-resolution (SR) of remote sensing images is essential to compensate for missing information in the original high-resolution (HR) images. Single-image super-resolution (SISR) technique aims to recover high-resolution images from low-resolution (LR) images. However, traditional SISR methods often result in blurred and unclear images due to the loss of high-frequency details in LR images at high magnifications. In this paper, a super-segmental reconstruction model STGAN for remote sensing images is proposed, which fuses the Generative Adversarial Networks (GANs) and self-attention mechanism based on the Reference Super Resolution method (RefSR). The core module of the model consists of multiple CNN-Swin Transformer blocks (MCST), each of which consists of a CNN layer and a specific modified Swin Transformer, constituting the feature extraction channel. In image hypersegmentation reconstruction, the optimized and improved correlation attention block (RAM-V) uses feature maps and gradient maps to improve the robustness of the model under different scenarios (such as land cover change). The experimental results show that the STGAN model proposed in this paper exhibits the best image data perception quality results with the best performance of LPIPS and PI metrics in the test set under RRSSRD public datasets. In the experimental test set, the PSNR reaches 31.4151, the SSIM is 0.8408, and the performance on the RMSE and SAM metrics is excellent, which demonstrate the model’s superior image reconstruction details in super-resolution reconstruction and highlighting the great potential of RefSR’s application to the task of super-scalar processing of remotely sensed images.https://www.mdpi.com/2076-3417/15/1/305artificial intelligenceconvolutional neural networksgenerative adversarial networkremote sensingswin transformersuper-resolution reconstruction |
| spellingShingle | Wei Huo Xiaodan Zhang Shaojie You Yongkun Zhang Qiyuan Zhang Naihao Hu STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction Applied Sciences artificial intelligence convolutional neural networks generative adversarial network remote sensing swin transformer super-resolution reconstruction |
| title | STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction |
| title_full | STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction |
| title_fullStr | STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction |
| title_full_unstemmed | STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction |
| title_short | STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction |
| title_sort | stgan swin transformer based gan to achieve remote sensing image super resolution reconstruction |
| topic | artificial intelligence convolutional neural networks generative adversarial network remote sensing swin transformer super-resolution reconstruction |
| url | https://www.mdpi.com/2076-3417/15/1/305 |
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