# PSGAN: A Generative Adversarial Network for Remote Sensing Image Pan-Sharpening

arXiv: https://arxiv.org/abs/1805.03371 · HTML: https://mlsanity.com/paper/1805.03371/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 May 2018.

## Built on by (most cited first, 24 of 24)

- [Generative Artificial Intelligence: A Systematic Review and Applications](https://mlsanity.com/paper/2405.11029/) (2024)
- [Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2109.02079/) (2021)
- [PanFormer: a Transformer Based Model for Pan-sharpening](https://mlsanity.com/paper/2203.02916/) (2022)
- [Pansharpening by convolutional neural networks in the full resolution framework](https://mlsanity.com/paper/2111.08334/) (2021)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Image Fusion in Remote Sensing: An Overview and Meta Analysis](https://mlsanity.com/paper/2401.08837/) (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)
- [Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening](https://mlsanity.com/paper/2109.09395/) (2021)
- [Variational Zero-shot Multispectral Pansharpening](https://mlsanity.com/paper/2407.06633/) (2024)
- [CD-GAN: a robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors](https://mlsanity.com/paper/2203.00948/) (2022)
- [Multi-Head Attention Residual Unfolded Network for Model-Based Pansharpening](https://mlsanity.com/paper/2409.02675/) (2024)
- [LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes](https://mlsanity.com/paper/2111.12483/) (2021)
- [PanBench: Towards High-Resolution and High-Performance Pansharpening](https://mlsanity.com/paper/2311.12083/) (2023)
- [Leveraging Large-Scale Pretrained Spatial-Spectral Priors for General Zero-Shot Pansharpening](https://mlsanity.com/paper/2512.02643/) (2025)
- [Learning deep multiresolution representations for pansharpening](https://mlsanity.com/paper/2102.08423/) (2021)
- [MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening](https://mlsanity.com/paper/2109.09937/) (2021)
- [Analytical Interpretation of Latent Codes in InfoGAN with SAR Images](https://mlsanity.com/paper/2205.13294/) (2022)
- [PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening](https://mlsanity.com/paper/2207.14451/) (2022)
- [Guided Deep Generative Model-based Spatial Regularization for Multiband Imaging Inverse Problems](https://mlsanity.com/paper/2306.17197/) (2023)
- [3D EAGAN: 3D edge-aware attention generative adversarial network for prostate segmentation in transrectal ultrasound images](https://mlsanity.com/paper/2311.04049/) (2023)
- [psPRF:Pansharpening Planar Neural Radiance Field for Generalized 3D Reconstruction Satellite Imagery](https://mlsanity.com/paper/2406.15707/) (2024)
- [Progressive Alignment Degradation Learning for Pansharpening](https://mlsanity.com/paper/2506.20179/) (2025)
- [GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening](https://mlsanity.com/paper/2606.17722/) (2026)

## Abstract (arXiv)

This paper addresses the problem of remote sensing image pan-sharpening from the perspective of generative adversarial learning. We propose a novel deep neural network based method named PSGAN. To the best of our knowledge, this is one of the first attempts at producing high-quality pan-sharpened images with GANs. The PSGAN consists of two components: a generative network (i.e., generator) and a discriminative network (i.e., discriminator). The generator is designed to accept panchromatic (PAN) and multispectral (MS) images as inputs and maps them to the desired high-resolution (HR) MS images and the discriminator implements the adversarial training strategy for generating higher fidelity pan-sharpened images. In this paper, we evaluate several architectures and designs, namely two-stream input, stacking input, batch normalization layer, and attention mechanism to find the optimal solution for pan-sharpening. Extensive experiments on QuickBird, GaoFen-2, and WorldView-2 satellite images demonstrate that the proposed PSGANs not only are effective in generating high-quality HR MS images and superior to state-of-the-art methods and also generalize well to full-scale images.