A Survey on Feature Diversity Enhancement Techniques for Remote Sensing Video Super-Resolution

Remote sensing video super-resolution (VSR) is a vital technology enabling fine-grained Earth observation from satellites. With growing demands in applications such as envi ronmental monitoring, urban development, and disaster management, improving the resolution of remote sensing videos has become paramount. Traditional video super-resolution methods, designed primarily for natural scenes, often fail to address the unique challenges posed by satellite imagery. This survey comprehensively reviews recent developments in feature diversity enhancement for VSR, focusing on the challenges of spatial, chan- nel, and temporal heterogeneity. We place particular emphasis on MADNet, a novel architecture that integrates Spatial Diversity Enhancement (SDE) and Channel Diversity Enhancement (CDE) into a Multi-Axis Diversity Module (MADM). Furthermore, we compare MADNet with state-of-the-art VSR models, analyze its architectural innovations, and identify future research directions. This paper aims to serve as a foundational resource for re- searchers and practitioners interested in highfidelity satellite video reconstruction