# Target-adaptive CNN-based pansharpening

arXiv: https://arxiv.org/abs/1709.06054 · HTML: https://mlsanity.com/paper/1709.06054/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2017.

## Built on by (most cited first, 20 of 20)

- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Pansharpening by convolutional neural networks in the full resolution framework](https://mlsanity.com/paper/2111.08334/) (2021)
- [Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2110.11591/) (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)
- [A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2311.08955/) (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)
- [Test-time Training for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2409.08667/) (2024)
- [LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes](https://mlsanity.com/paper/2111.12483/) (2021)
- [A full-resolution training framework for Sentinel-2 image fusion](https://mlsanity.com/paper/2307.14864/) (2023)
- [Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control](https://mlsanity.com/paper/2505.16658/) (2025)
- [Learning deep multiresolution representations for pansharpening](https://mlsanity.com/paper/2102.08423/) (2021)
- [Pan-sharpening via High-pass Modification Convolutional Neural Network](https://mlsanity.com/paper/2105.11576/) (2021)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Efficient and Accurate Hyperspectral Pansharpening Using 3D VolumeNet and 2.5D Texture Transfer](https://mlsanity.com/paper/2203.03951/) (2022)
- [PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening](https://mlsanity.com/paper/2207.14451/) (2022)
- [Understanding Pan-Sharpening via Generalized Inverse](https://mlsanity.com/paper/2310.02718/) (2023)
- [DSConv: Dynamic Splitting Convolution for Pansharpening](https://mlsanity.com/paper/2508.06147/) (2025)
- [GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening](https://mlsanity.com/paper/2606.17722/) (2026)

## Abstract (arXiv)

We recently proposed a convolutional neural network (CNN) for remote sensing image pansharpening obtaining a significant performance gain over the state of the art. In this paper, we explore a number of architectural and training variations to this baseline, achieving further performance gains with a lightweight network which trains very fast. Leveraging on this latter property, we propose a target-adaptive usage modality which ensures a very good performance also in the presence of a mismatch w.r.t. the training set, and even across different sensors. The proposed method, published online as an off-the-shelf software tool, allows users to perform fast and high-quality CNN-based pansharpening of their own target images on general-purpose hardware.