# Image Fusion in Remote Sensing: An Overview and Meta Analysis

arXiv: https://arxiv.org/abs/2401.08837 · HTML: https://mlsanity.com/paper/2401.08837/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 265 references that are in the map.
- First posted on arXiv 16 Jan 2024.

## Builds on (most cited first, 17 of 17)

- [Pyramid Stereo Matching Network](https://mlsanity.com/paper/1803.08669/) (2018)
- [Transformers in Remote Sensing: A Survey](https://mlsanity.com/paper/2209.01206/) (2022)
- [Learning Spectral-Spatial-Temporal Features via a Recurrent Convolutional Neural Network for Change Detection in Multispectral Imagery](https://mlsanity.com/paper/1803.02642/) (2018)
- [PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening](https://mlsanity.com/paper/1805.03371/) (2018)
- [Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network](https://mlsanity.com/paper/1705.07556/) (2017)
- [Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks](https://mlsanity.com/paper/2007.03107/) (2020)
- [Unsupervised Image Regression for Heterogeneous Change Detection](https://mlsanity.com/paper/1909.05948/) (2019)
- [Deep Learning for Multiple-Image Super-Resolution](https://mlsanity.com/paper/1903.00440/) (2019)
- [DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images](https://mlsanity.com/paper/2005.06216/) (2020)
- [ResDepth: Learned Residual Stereo Reconstruction](https://mlsanity.com/paper/2001.08026/) (2020)
- [Automated 3D recovery from very high resolution multi-view satellite images](https://mlsanity.com/paper/1905.07475/) (2019)
- [Vehicle Detection of Multi-source Remote Sensing Data Using Active Fine-tuning Network](https://mlsanity.com/paper/2007.08494/) (2020)
- [Recurrent Multiresolution Convolutional Networks for VHR Image Classification](https://mlsanity.com/paper/1806.05793/) (2018)
- [Recurrent Neural Networks to Correct Satellite Image Classification Maps](https://mlsanity.com/paper/1608.03440/) (2016)
- [A Spatial and Temporal Non-Local Filter Based Data Fusion](https://mlsanity.com/paper/1611.07231/) (2016)
- [3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets](https://mlsanity.com/paper/2107.00590/) (2021)
- [Generating gapless land surface temperature with a high spatio-temporal resolution by fusing multi-source satellite-observed and model-simulated data](https://mlsanity.com/paper/2111.15636/) (2021)

## Built on by (most cited first, 10 of 10)

- [Task-driven Image Fusion with Learnable Fusion Loss](https://mlsanity.com/paper/2412.03240/) (2024)
- [3MOS: Multi-sources, Multi-resolutions, and Multi-scenes dataset for Optical-SAR image matching](https://mlsanity.com/paper/2404.00838/) (2024)
- [An Evaluation of DUSt3R/MASt3R/VGGT 3D Reconstruction on Photogrammetric Aerial Blocks](https://mlsanity.com/paper/2507.14798/) (2025)
- [Distilling Textual Priors from LLM to Efficient Image Fusion](https://mlsanity.com/paper/2504.07029/) (2025)
- [SFNet: Fusion of Spatial and Frequency-Domain Features for Remote Sensing Image Forgery Detection](https://mlsanity.com/paper/2506.20599/) (2025)
- [SGMA: Semantic-Guided Modality-Aware Segmentation for Remote Sensing with Incomplete Multimodal Data](https://mlsanity.com/paper/2603.02505/) (2026)
- [Copy-Move Forgery Detection and Question Answering for Remote Sensing Image](https://mlsanity.com/paper/2412.02575/) (2024)
- [CTP: A hybrid CNN-Transformer-PINN model for ocean front forecasting](https://mlsanity.com/paper/2505.10894/) (2025)
- [Hybrid Fusion: One-Minute Efficient Training for Zero-Shot Cross-Domain Image Fusion](https://mlsanity.com/paper/2602.20851/) (2026)
- [Parameterized Quantum Circuits as Feature Maps: Representation Quality and Readout Effects in Multispectral Land-Cover Classification](https://mlsanity.com/paper/2604.26675/) (2026)

## Abstract (arXiv)

Image fusion in Remote Sensing (RS) has been a consistent demand due to its ability to turn raw images of different resolutions, sources, and modalities into accurate, complete, and spatio-temporally coherent images. It greatly facilitates downstream applications such as pan-sharpening, change detection, land-cover classification, etc. Yet, image fusion solutions are highly disparate to various remote sensing problems and thus are often narrowly defined in existing reviews as topical applications, such as pan-sharpening, and spatial-temporal image fusion. Considering that image fusion can be theoretically applied to any gridded data through pixel-level operations, in this paper, we expanded its scope by comprehensively surveying relevant works with a simple taxonomy: 1) many-to-one image fusion; 2) many-to-many image fusion. This simple taxonomy defines image fusion as a mapping problem that turns either a single or a set of images into another single or set of images, depending on the desired coherence, e.g., spectral, spatial/resolution coherence, etc. We show that this simple taxonomy, despite the significant modality difference it covers, can be presented by a conceptually easy framework. In addition, we provide a meta-analysis to review the major papers studying the various types of image fusion and their applications over the years (from the 1980s to date), covering 5,926 peer-reviewed papers. Finally, we discuss the main benefits and emerging challenges to provide open research directions and potential future works.