# Pansharpening by convolutional neural networks in the full resolution framework

arXiv: https://arxiv.org/abs/2111.08334 · HTML: https://mlsanity.com/paper/2111.08334/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 100 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 11 of 11)

- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Image Super-Resolution Using Deep Convolutional Networks](https://mlsanity.com/paper/1501.00092/) (2014)
- [Object Detection with Deep Learning: A Review](https://mlsanity.com/paper/1807.05511/) (2018)
- [An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing](https://mlsanity.com/paper/1810.12000/) (2018)
- [A Multi-Scale and Multi-Depth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening](https://mlsanity.com/paper/1712.09809/) (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)
- [Target-adaptive CNN-based pansharpening](https://mlsanity.com/paper/1709.06054/) (2017)
- [CoSpace: Common Subspace Learning from Hyperspectral-Multispectral Correspondences](https://mlsanity.com/paper/1812.11501/) (2018)
- [PGMAN: An Unsupervised Generative Multi-adversarial Network for Pan-sharpening](https://mlsanity.com/paper/2012.09054/) (2020)
- [Full-resolution quality assessment for pansharpening](https://mlsanity.com/paper/2108.06144/) (2021)

## Built on by (most cited first, 15 of 15)

- [Unsupervised Deep Learning-based Pansharpening with Jointly-Enhanced Spectral and Spatial Fidelity](https://mlsanity.com/paper/2307.14403/) (2023)
- [Hyperspectral Pansharpening: Critical Review, Tools and Future Perspectives](https://mlsanity.com/paper/2407.01355/) (2024)
- [Band-wise Hyperspectral Image Pansharpening using CNN Model Propagation](https://mlsanity.com/paper/2311.06510/) (2023)
- [Perspective-Equivariance for Unsupervised Imaging with Camera Geometry](https://mlsanity.com/paper/2403.09327/) (2024)
- [U-Know-DiffPAN: An Uncertainty-aware Knowledge Distillation Diffusion Framework with Details Enhancement for PAN-Sharpening](https://mlsanity.com/paper/2412.06243/) (2024)
- [SMART-Ship: A Comprehensive Synchronized Multi-modal Aligned Remote Sensing Targets Dataset and Benchmark for Berthed Ships Analysis](https://mlsanity.com/paper/2508.02384/) (2025)
- [EOGS++: Earth Observation Gaussian Splatting with Internal Camera Refinement and Direct Panchromatic Rendering](https://mlsanity.com/paper/2511.16542/) (2025)
- [Enpowering Your Pansharpening Models with Generalizability: Unified Distribution is All You Need](https://mlsanity.com/paper/2510.22217/) (2025)
- [Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control](https://mlsanity.com/paper/2505.16658/) (2025)
- [G-ZAP: A Generalizable Zero-Shot Framework for Arbitrary-Scale Pansharpening](https://mlsanity.com/paper/2603.14412/) (2026)
- [Enhancing DeepLabV3+ to Fuse Aerial and Satellite Images for Semantic Segmentation](https://mlsanity.com/paper/2503.22909/) (2025)
- [Two-Stage Random Alternation Framework for One-Shot Pansharpening](https://mlsanity.com/paper/2505.06576/) (2025)
- [Progressive Alignment Degradation Learning for Pansharpening](https://mlsanity.com/paper/2506.20179/) (2025)
- [CC-Pan: Channel-wise Compression based Diffusion for Efficient Pan-Sharpening](https://mlsanity.com/paper/2602.04473/) (2026)
- [Fast Model-guided Instance-wise Adaptation Framework for Real-world Pansharpening with Fidelity Constraints](https://mlsanity.com/paper/2604.08903/) (2026)

## Abstract (arXiv)

In recent years, there has been a growing interest in deep learning-based pansharpening. Thus far, research has mainly focused on architectures. Nonetheless, model training is an equally important issue. A first problem is the absence of ground truths, unavoidable in pansharpening. This is often addressed by training networks in a reduced resolution domain and using the original data as ground truth, relying on an implicit scale invariance assumption. However, on full resolution images results are often disappointing, suggesting such invariance not to hold. A further problem is the scarcity of training data, which causes a limited generalization ability and a poor performance on off-training test images. In this paper, we propose a full-resolution training framework for deep learning-based pansharpening. The framework is fully general and can be used for any deep learning-based pansharpening model. Training takes place in the high-resolution domain, relying only on the original data, thus avoiding any loss of information. To ensure spectral and spatial fidelity, a suitable two-component loss is defined. The spectral component enforces consistency between the pansharpened output and the low-resolution multispectral input. The spatial component, computed at high-resolution, maximizes the local correlation between each pansharpened band and the panchromatic input. At testing time, the target-adaptive operating modality is adopted, achieving good generalization with a limited computational overhead. Experiments carried out on WorldView-3, WorldView-2, and GeoEye-1 images show that methods trained with the proposed framework guarantee a pretty good performance in terms of both full-resolution numerical indexes and visual quality.