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Super-resolution (SR) of remote sensing images is essential to compensate for
missing information in the original high-resolution (HR) images. Single-image superresolution (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..
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