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

arXiv: https://arxiv.org/abs/2505.23248 · HTML: https://mlsanity.com/paper/2505.23248/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 46 of its 409 references that are in the map.
- First posted on arXiv 29 May 2025.

## Builds on (most cited first, 30 of 46)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [Remote Sensing Image Scene Classification: Benchmark and State of the Art](https://mlsanity.com/paper/1703.00121/) (2017)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [DOTA: A Large-scale Dataset for Object Detection in Aerial Images](https://mlsanity.com/paper/1711.10398/) (2017)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [Deep Learning for Image Super-resolution: A Survey](https://mlsanity.com/paper/1902.06068/) (2019)
- [AID: A Benchmark Dataset for Performance Evaluation of Aerial Scene Classification](https://mlsanity.com/paper/1608.05167/) (2016)
- [The 2018 PIRM Challenge on Perceptual Image Super-resolution](https://mlsanity.com/paper/1809.07517/) (2018)
- [Learning a No-Reference Quality Metric for Single-Image Super-Resolution](https://mlsanity.com/paper/1612.05890/) (2016)
- [PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval](https://mlsanity.com/paper/1706.03424/) (2017)
- [Pan-Mamba: Effective pan-sharpening with State Space Model](https://mlsanity.com/paper/2402.12192/) (2024)
- [Fusion-Mamba for Cross-modality Object Detection](https://mlsanity.com/paper/2404.09146/) (2024)
- [EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2310.19288/) (2023)
- [Hyperspectral pansharpening: a review](https://mlsanity.com/paper/1504.04531/) (2015)
- [Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution](https://mlsanity.com/paper/2405.04964/) (2024)
- [Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network](https://mlsanity.com/paper/1803.04271/) (2018)
- [Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2109.02079/) (2021)
- [OverLoCK: An Overview-first-Look-Closely-next ConvNet with Context-Mixing Dynamic Kernels](https://mlsanity.com/paper/2502.20087/) (2025)
- [Simulation-based Lidar Super-resolution for Ground Vehicles](https://mlsanity.com/paper/2004.05242/) (2020)
- [Decoupled-and-Coupled Networks: Self-Supervised Hyperspectral Image Super-Resolution with Subpixel Fusion](https://mlsanity.com/paper/2205.03742/) (2022)
- [Remote Sensing Image Super-resolution and Object Detection: Benchmark and State of the Art](https://mlsanity.com/paper/2111.03260/) (2021)

## Built on by (most cited first, 8 of 8)

- [Semantic-Guided Cross-Sensor Super Resolution of Remote Sensing Images: A Gated Dual Conditioning Flow Matching Model](https://mlsanity.com/paper/2510.23816/) (2025)
- [Agentic AI-Empowered Dynamic Survey Framework](https://mlsanity.com/paper/2602.04071/) (2026)
- [Beyond Visual Fidelity: Benchmarking Super-Resolution Models for Large-Scale Remote Sensing Imagery via Downstream Task Integration](https://mlsanity.com/paper/2605.00310/) (2026)
- [FLNet: Flood-Induced Agriculture Damage Assessment using Super Resolution of Satellite Images](https://mlsanity.com/paper/2601.03884/) (2026)
- [Self-Supervised Super-Resolution for Sentinel-5P Hyperspectral Images](https://mlsanity.com/paper/2604.17652/) (2026)
- [Spatial-Frequency Gated Swin Transformer for Cross-Sensor Remote Sensing Super-Resolution](https://mlsanity.com/paper/2605.09687/) (2026)
- [Physics-Guided Deep Unfolding for Blind Cross-Sensor Spectral Super-Resolution via Learning the Spectral Transformation Function](https://mlsanity.com/paper/2606.05759/) (2026)
- [Mind the Gap: Quantifying the Domain Gap in Cross-Sensor Diffusion Super-Resolution](https://mlsanity.com/paper/2606.28039/) (2026)

## Abstract (arXiv)

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.