# Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need

arXiv: https://arxiv.org/abs/2510.22217 · HTML: https://mlsanity.com/paper/2510.22217/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 94 references that are in the map.
- First posted on arXiv 25 Oct 2025.

## Builds on (most cited first, 19 of 19)

- [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)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Computational Optimal Transport](https://mlsanity.com/paper/1803.00567/) (2018)
- [On Wasserstein Two Sample Testing and Related Families of Nonparametric Tests](https://mlsanity.com/paper/1509.02237/) (2015)
- [Pan-Mamba: Effective pan-sharpening with State Space Model](https://mlsanity.com/paper/2402.12192/) (2024)
- [A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening](https://mlsanity.com/paper/1712.09809/) (2017)
- [Deep Gradient Projection Networks for Pan-sharpening](https://mlsanity.com/paper/2103.04584/) (2021)
- [FusionMamba: Efficient Remote Sensing Image Fusion with State Space Model](https://mlsanity.com/paper/2404.07932/) (2024)
- [Unsupervised Hyperspectral Pansharpening via Low-rank Diffusion Model](https://mlsanity.com/paper/2305.10925/) (2023)
- [Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening](https://mlsanity.com/paper/2404.07543/) (2024)
- [Pansharpening by convolutional neural networks in the full resolution framework](https://mlsanity.com/paper/2111.08334/) (2021)
- [SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening](https://mlsanity.com/paper/2404.11537/) (2024)
- [Frequency-Adaptive Pan-Sharpening with Mixture of Experts](https://mlsanity.com/paper/2401.02151/) (2024)
- [Fourier-enhanced Implicit Neural Fusion Network for Multispectral and Hyperspectral Image Fusion](https://mlsanity.com/paper/2404.15174/) (2024)
- [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)
- [CrossDiff: Exploring Self-Supervised Representation of Pansharpening via Cross-Predictive Diffusion Model](https://mlsanity.com/paper/2401.05153/) (2024)
- [Variational Zero-shot Multispectral Pansharpening](https://mlsanity.com/paper/2407.06633/) (2024)

## Built on by (most cited first, 2 of 2)

- [A Unified Foundation Model for All-in-One Multi-Modal Remote Sensing Image Restoration and Fusion with Language Prompting](https://mlsanity.com/paper/2604.05629/) (2026)
- [Universal Pansharpening Model](https://mlsanity.com/paper/2603.03831/) (2026)

## Abstract (arXiv)

Existing deep learning-based models for remote sensing pansharpening exhibit exceptional performance on training datasets. However, due to sensor-specific characteristics and varying imaging conditions, these models suffer from substantial performance degradation when applied to unseen satellite data, lacking generalizability and thus limiting their applicability. We argue that the performance drops stem primarily from distributional discrepancies from different sources and the key to addressing this challenge lies in bridging the gap between training and testing distributions. To validate the idea and further achieve a "train once, deploy forever" capability, this paper introduces a novel and intuitive approach to enpower any pansharpening models with generalizability by employing a unified distribution strategy (UniPAN). Specifically, we construct a distribution transformation function that normalizes the pixels sampled from different sources to conform to an identical distribution. The deep models are trained on the transformed domain, and during testing on new datasets, the new data are also transformed to match the training distribution. UniPAN aims to train and test the model on a unified and consistent distribution, thereby enhancing its generalizability. Extensive experiments validate the efficacy of UniPAN, demonstrating its potential to significantly enhance the performance of deep pansharpening models across diverse satellite sensors. Codes: https://github.com/yc-cui/UniPAN.