# Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives

arXiv: https://arxiv.org/abs/2407.01355 · HTML: https://mlsanity.com/paper/2407.01355/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 117 references that are in the map.
- First posted on arXiv 1 Jul 2024.

## Builds on (most cited first, 22 of 22)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Deep Image Prior](https://mlsanity.com/paper/1711.10925/) (2017)
- [Deep Learning for Hyperspectral Image Classification: An Overview](https://mlsanity.com/paper/1910.12861/) (2019)
- [Hyperspectral pansharpening: a review](https://mlsanity.com/paper/1504.04531/) (2015)
- [Deep Learning for Classification of Hyperspectral Data: A Comparative Review](https://mlsanity.com/paper/1904.10674/) (2019)
- [A convex formulation for hyperspectral image superresolution via subspace-based regularization](https://mlsanity.com/paper/1411.4005/) (2014)
- [HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening](https://mlsanity.com/paper/2203.02503/) (2022)
- [Fast Fusion of Multi-Band Images Based on Solving a Sylvester Equation](https://mlsanity.com/paper/1502.03121/) (2015)
- [Hyperspectral and Multispectral Image Fusion based on a Sparse Representation](https://mlsanity.com/paper/1409.5729/) (2014)
- [Target-adaptive CNN-based pansharpening](https://mlsanity.com/paper/1709.06054/) (2017)
- [Guided Deep Decoder: Unsupervised Image Pair Fusion](https://mlsanity.com/paper/2007.11766/) (2020)
- [Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model](https://mlsanity.com/paper/2305.10925/) (2023)
- [Pansharpening by convolutional neural networks in the full resolution framework](https://mlsanity.com/paper/2111.08334/) (2021)
- [Bayesian Fusion of Multi-Band Images](https://mlsanity.com/paper/1307.5996/) (2013)
- [Hyperspectral Pansharpening Based on Improved Deep Image Prior and Residual Reconstruction](https://mlsanity.com/paper/2107.02630/) (2021)
- [Full-resolution quality assessment for pansharpening](https://mlsanity.com/paper/2108.06144/) (2021)
- [Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity](https://mlsanity.com/paper/2307.14403/) (2023)
- [Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation](https://mlsanity.com/paper/2311.06510/) (2023)
- [A full-resolution training framework for Sentinel-2 image fusion](https://mlsanity.com/paper/2307.14864/) (2023)

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

- [Taming Flow Matching with Unbalanced Optimal Transport into Fast Pansharpening](https://mlsanity.com/paper/2503.14975/) (2025)
- [U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening](https://mlsanity.com/paper/2412.06243/) (2024)
- [Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control](https://mlsanity.com/paper/2505.16658/) (2025)
- [Two-Stage Random Alternation Framework for One-Shot Pansharpening](https://mlsanity.com/paper/2505.06576/) (2025)
- [Universal Pansharpening Model](https://mlsanity.com/paper/2603.03831/) (2026)
- [Dual Modality Prompted Diffusion Priors for Zero Shot Hyperspectral Pansharpening](https://mlsanity.com/paper/2608.11748/) (2026)
- [Lightweight Interpretable RGB-Guided Hyperspectral Super-Resolution under Real Cross-resolution Misalignment](https://mlsanity.com/paper/2609.01060/) (2026)
- [Super-Resolving Unseen Hyperspectral Sensors at Any Scale via Spatial Operators](https://mlsanity.com/paper/2609.39926/) (2026)

## Abstract (arXiv)

Hyperspectral pansharpening consists of fusing a high-resolution panchromatic band and a low-resolution hyperspectral image to obtain a new image with high resolution in both the spatial and spectral domains. These remote sensing products are valuable for a wide range of applications, driving ever growing research efforts. Nonetheless, results still do not meet application demands. In part, this comes from the technical complexity of the task: compared to multispectral pansharpening, many more bands are involved, in a spectral range only partially covered by the panchromatic component and with overwhelming noise. However, another major limiting factor is the absence of a comprehensive framework for the rapid development and accurate evaluation of new methods. This paper attempts to address this issue. We started by designing a dataset large and diverse enough to allow reliable training (for data-driven methods) and testing of new methods. Then, we selected a set of state-of-the-art methods, following different approaches, characterized by promising performance, and reimplemented them in a single PyTorch framework. Finally, we carried out a critical comparative analysis of all methods, using the most accredited quality indicators. The analysis highlights the main limitations of current solutions in terms of spectral/spatial quality and computational efficiency, and suggests promising research directions. To ensure full reproducibility of the results and support future research, the framework (including codes, evaluation procedures and links to the dataset) is shared on https://github.com/matciotola/hyperspectral_pansharpening_toolbox, as a single Python-based reference benchmark toolbox.