# Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening

arXiv: https://arxiv.org/abs/2109.09395 · HTML: https://mlsanity.com/paper/2109.09395/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 46 references that are in the map.
- First posted on arXiv 20 Sep 2021.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://mlsanity.com/paper/1511.06434/) (2015)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Instance Normalization: The Missing Ingredient for Fast Stylization](https://mlsanity.com/paper/1607.08022/) (2016)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Self-Attention Generative Adversarial Networks](https://mlsanity.com/paper/1805.08318/) (2018)
- [A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening](https://mlsanity.com/paper/1712.09809/) (2017)
- [Remote Sensing Image Fusion Based on Two-stream Fusion Network](https://mlsanity.com/paper/1711.02549/) (2017)
- [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)
- [PGMAN: An Unsupervised Generative Multi-adversarial Network for Pan-sharpening](https://mlsanity.com/paper/2012.09054/) (2020)
- [Unsupervised Pansharpening Based on Self-Attention Mechanism](https://mlsanity.com/paper/2006.09303/) (2020)
- [Residual Channel Attention Generative Adversarial Network for Image Super-Resolution and Noise Reduction](https://mlsanity.com/paper/2004.13674/) (2020)
- [MsCGAN: Multi-scale Conditional Generative Adversarial Networks for Person Image Generation](https://mlsanity.com/paper/1810.08534/) (2018)

## Built on by (most cited first, 5 of 5)

- [Variational Zero-shot Multispectral Pansharpening](https://mlsanity.com/paper/2407.06633/) (2024)
- [Pan-LUT: Efficient Pan-sharpening via Learnable Look-Up Tables](https://mlsanity.com/paper/2503.23793/) (2025)
- [SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpening](https://mlsanity.com/paper/2307.00327/) (2023)
- [A Benchmarking Protocol for SAR Colorization: From Regression to Deep Learning Approaches](https://mlsanity.com/paper/2310.08705/) (2023)
- [Hyperspectral Image Fusion with Spectral-Band and Fusion-Scale Agnosticism](https://mlsanity.com/paper/2602.01681/) (2026)

## Abstract (arXiv)

Deep learning based pan-sharpening has received significant research interest in recent years. Most of existing methods fall into the supervised learning framework in which they down-sample the multi-spectral (MS) and panchromatic (PAN) images and regard the original MS images as ground truths to form training samples. Although impressive performance could be achieved, they have difficulties generalizing to the original full-scale images due to the scale gap, which makes them lack of practicability. In this paper, we propose an unsupervised generative adversarial framework that learns from the full-scale images without the ground truths to alleviate this problem. We extract the modality-specific features from the PAN and MS images with a two-stream generator, perform fusion in the feature domain, and then reconstruct the pan-sharpened images. Furthermore, we introduce a novel hybrid loss based on the cycle-consistency and adversarial scheme to improve the performance. Comparison experiments with the state-of-the-art methods are conducted on GaoFen-2 and WorldView-3 satellites. Results demonstrate that the proposed method can greatly improve the pan-sharpening performance on the full-scale images, which clearly show its practical value. Codes are available at https://github.com/zhysora/UCGAN.