MODE-GAN: Multi-order dual-edge generative adversarial network for remote sensing image super-resolution

The spatial resolution of remote sensing imagery is intrinsically limited by satellite sensor constraints and atmospheric degradation. To overcome these challenges, we propose the Multi-Order Dual-Edge Generative Adversarial Network (MODE-GAN). Our framework incorporates a novel Edge-Aware Feature Module (EAFM), which integrates first-order Scharr gradients with second-order multi-scale Laplacian of Gaussian (mLoG) operators as physics-inspired guidance. To ensure physical consistency for quantitative analysis, we employ the Spectral Angle Mapper (SAM) as a critical validation metric to assess radiometric fidelity. We validated the model using 12,996 real-world, cross-sensor pairs from Landsat-8 and Sentinel-2. Experimental results demonstrate that MODE-GAN significantly outperforms state-of-the-art architectures. Quantitatively, the model achieves a PSNR of 24.30 dB and an SSIM of 0.77, outperforming the second-best model by 0.76 dB and 0.03, respectively. Notably, MODE-GAN preserves the intrinsic spectral shape with a SAM value of 7.48, superior to HAT (8.53) and Real-ESRGAN (8.11). Stress tests confirm high resilience to sub-pixel registration noise, ensuring structural integrity in non-aligned scenarios. By integrating multi-order differential physical operators as internal feature guidance and structural loss constraints, MODE-GAN achieves enhanced geometric structure and spectral fidelity, providing a reliable pathway for downstream quantitative applications such as NDVI estimation and land-cover mapping.