# Recent Advances and New Guidelines on Hyperspectral and Multispectral Image Fusion

arXiv: https://arxiv.org/abs/2008.03426 · HTML: https://mlsanity.com/paper/2008.03426/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Aug 2020.

## Built on by (most cited first, 21 of 21)

- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2109.02079/) (2021)
- [FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise Removal](https://mlsanity.com/paper/2109.08879/) (2021)
- [Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2110.11591/) (2021)
- [Learning A 3D-CNN and Transformer Prior for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2111.13923/) (2021)
- [A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2311.08955/) (2023)
- [LADMM-Net: An Unrolled Deep Network For Spectral Image Fusion From Compressive Data](https://mlsanity.com/paper/2103.00940/) (2021)
- [Limitations of Data-Driven Spectral Reconstruction -- An Optics-Aware Analysis](https://mlsanity.com/paper/2401.03835/) (2024)
- [Hyperspectral and Multispectral Image Fusion Using the Conditional Denoising Diffusion Probabilistic Model](https://mlsanity.com/paper/2307.03423/) (2023)
- [Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning](https://mlsanity.com/paper/2409.09670/) (2024)
- [Asymmetric Hash Code Learning for Remote Sensing Image Retrieval](https://mlsanity.com/paper/2201.05772/) (2022)
- [Physics-Inspired Degradation Models for Hyperspectral Image Fusion](https://mlsanity.com/paper/2402.02411/) (2024)
- [Hyperspectral Anomaly Detection Fused Unified Nonconvex Tensor Ring Factors Regularization](https://mlsanity.com/paper/2505.17881/) (2025)
- [CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image Fusion](https://mlsanity.com/paper/2406.19666/) (2024)
- [HyFusion: Enhanced Reception Field Transformer for Hyperspectral Image Fusion](https://mlsanity.com/paper/2501.04665/) (2025)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)
- [Unsupervised Hyperspectral and Multispectral Images Fusion Based on the Cycle Consistency](https://mlsanity.com/paper/2307.03413/) (2023)
- [UnmixingSR: Material-aware Network with Unsupervised Unmixing as Auxiliary Task for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2407.06525/) (2024)
- [SpecSwin3D: Generating Hyperspectral Imagery from Multispectral Data via Transformer Networks](https://mlsanity.com/paper/2509.06122/) (2025)
- [Registration-Free Hyperspectral Reconstruction from RGB via a Permutation-Invariant Gram-Matrix Principle](https://mlsanity.com/paper/2608.14994/) (2026)
- [Learning Spatial-Spectral Refinement and Calibrating Complementary Observations for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2609.05303/) (2026)

## Abstract (arXiv)

Hyperspectral image (HSI) with high spectral resolution often suffers from low spatial resolution owing to the limitations of imaging sensors. Image fusion is an effective and economical way to enhance the spatial resolution of HSI, which combines HSI with higher spatial resolution multispectral image (MSI) of the same scenario. In the past years, many HSI and MSI fusion algorithms are introduced to obtain high-resolution HSI. However, it lacks a full-scale review for the newly proposed HSI and MSI fusion approaches. To tackle this problem,this work gives a comprehensive review and new guidelines for HSI-MSI fusion. According to the characteristics of HSI-MSI fusion methods, they are categorized as four categories, including pan-sharpening based approaches, matrix factorization based approaches, tensor representation based approaches, and deep convolution neural network based approaches. We make a detailed introduction, discussions, and comparison for the fusion methods in each category. Additionally, the existing challenges and possible future directions for the HSI-MSI fusion are presented.