系统梳理遥感图像超分辨率技术，揭示现有方法在细节保持上的短板。
Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey
- 按监督、无监督和质量评估三类梳理主流算法
- 发现现有方法在大尺度退化下难以保留纹理与结构
- 建议发展领域专用架构和更真实评估方案
遥感图像超分辨率（RSISR）是遥感图像处理中的关键任务，旨在从低分辨率（LR）图像重建高分辨率（HR）图像。尽管近年来提出众多RSISR方法，但系统性综述仍显不足。本文全面回顾了RSISR算法，涵盖方法学、数据集与评价指标。深入分析了监督式、无监督式及质量评估三类方法，帮助研究者理解当前趋势与挑战。研究表明，现有方法在大尺度退化下存在显著局限，尤其难以保持细粒度纹理与几何结构。基于此，本文提出未来方向：需发展领域特定架构与鲁棒评估协议，以弥合合成数据与真实遥感场景间的差距。
原文摘要 · Abstract (English)
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.
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