A Robust System for Super-Resolution Imaging in Remote Sensing via Attention-Based Residual Learning

Rogelio Reyes-Reyes, Yeredith G. Mora-Martinez, Beatriz P. Garcia-Salgado, Volodymyr Ponomaryov (), Jose A. Almaraz-Damian, Clara Cruz-Ramos and Sergiy Sadovnychiy
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Rogelio Reyes-Reyes: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacán, Mexico City 04440, Mexico
Yeredith G. Mora-Martinez: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacán, Mexico City 04440, Mexico
Beatriz P. Garcia-Salgado: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacán, Mexico City 04440, Mexico
Volodymyr Ponomaryov: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacán, Mexico City 04440, Mexico
Jose A. Almaraz-Damian: Centro de Investigación Científica y de Educación Superior de Ensenada, Unidad Académica Tepic, Tepic 63173, Mexico
Clara Cruz-Ramos: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Culhuacán, Mexico City 04440, Mexico
Sergiy Sadovnychiy: Instituto Mexicano del Petróleo, Mexico City 07730, Mexico

Mathematics, 2025, vol. 13, issue 15, 1-28

Abstract: Deep learning-based super-resolution (SR) frameworks are widely used in remote sensing applications. However, existing SR models still face limitations, particularly in recovering contours, fine features, and textures, as well as in effectively integrating channel information. To address these challenges, this study introduces a novel residual model named OARN (Optimized Attention Residual Network) specifically designed to enhance the visual quality of low-resolution images. The network operates on the Y channel of the YCbCr color space and integrates LKA (Large Kernel Attention) and OCM (Optimized Convolutional Module) blocks. These components can restore large-scale spatial relationships and refine textures and contours, improving feature reconstruction without significantly increasing computational complexity. The performance of OARN was evaluated using satellite images from WorldView-2, GaoFen-2, and Microsoft Virtual Earth. Evaluation was conducted using objective quality metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), Edge Preservation Index (EPI), and Perceptual Image Patch Similarity (LPIPS), demonstrating superior results compared to state-of-the-art methods in both objective measurements and subjective visual perception. Moreover, OARN achieves this performance while maintaining computational efficiency, offering a balanced trade-off between processing time and reconstruction quality.

Keywords: super-resolution; remote sensing; deep learning; balanced trade-off; Large Kernel Attention (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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