Abstract
Remote sensing images are characterized by high complexity, significant scale variations, and abundant details, which present challenges for existing deep learning-based super-resolution reconstruction methods. These algorithms often exhibit limited convolutional receptive fields and thus struggle to establish global contextual information, which can lead to an inadequate utilization of both global and local details and limited generalization capabilities. To address these issues, this study introduces a novel multi-branch residual hybrid attention block (MBRHAB). This innovative approach is part of a proposed super-resolution reconstruction model for remote sensing data, which incorporates various attention mechanisms to enhance performance. First, the model employs window-based multi-head self-attention to model long-range dependencies in images. A multi-branch convolution module (MBCM) is then constructed to enhance the convolutional receptive field for improved representation of global information. Convolutional attention is subsequently combined across channels and spatial dimensions to strengthen associations between different features and areas containing crucial details, thereby augmenting local semantic information. Finally, the model adopts a parallel design to enhance computational efficiency. Generalization performance was assessed using a cross-dataset approach involving two training datasets (NWPU-RESISC45 and PatternNet) and a third test dataset (UCMerced-LandUse). Experimental results confirmed that the proposed method surpassed the existing super-resolution algorithms, including Bicubic interpolation, SRCNN, ESRGAN, Real-ESRGAN, IRN, and DSSR in the metrics of PSNR and SSIM across various magnifications scales.