Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey

TL;DR

This comprehensive survey reviews recent remote sensing image super-resolution methods, analyzing their methodologies, datasets, and challenges, and highlights future research directions to improve real-world applicability.

Contribution

The paper provides the first systematic, in-depth review of RSISR algorithms, categorizing them and analyzing their strengths, limitations, and challenges.

Findings

Existing methods struggle with preserving fine textures and structures.

Significant gaps remain between synthetic benchmarks and real-world scenarios.

Future directions include domain-specific architectures and robust evaluation protocols.

Abstract

Remote sensing image super-resolution (RSISR) is a crucial task in remote sensing image processing, aiming to reconstruct high-resolution (HR) images from their low-resolution (LR) counterparts. Despite the growing number of RSISR methods proposed in recent years, a systematic and comprehensive review of these methods is still lacking. This paper presents a thorough review of RSISR algorithms, covering methodologies, datasets, and evaluation metrics. We provide an in-depth analysis of RSISR methods, categorizing them into supervised, unsupervised, and quality evaluation approaches, to help researchers understand current trends and challenges. Our review also discusses the strengths, limitations, and inherent challenges of these techniques. Notably, our analysis reveals significant limitations in existing methods, particularly in preserving fine-grained textures and geometric structures under large-scale degradation. Based on these findings, we outline future research directions, highlighting the need for domain-specific architectures and robust evaluation protocols to bridge the gap between synthetic and real-world RSISR scenarios.