# Boosting the accuracy of multi-spectral image pan-sharpening by learning a deep residual network

arXiv: https://arxiv.org/abs/1705.07556 · HTML: https://mlsanity.com/paper/1705.07556/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 May 2017.

## Built on by (most cited first, 23 of 23)

- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [Deep Gradient Projection Networks for Pan-sharpening](https://mlsanity.com/paper/2103.04584/) (2021)
- [HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening](https://mlsanity.com/paper/2203.02503/) (2022)
- [PanFormer: a Transformer Based Model for Pan-sharpening](https://mlsanity.com/paper/2203.02916/) (2022)
- [PanFlowNet: A Flow-Based Deep Network for Pan-sharpening](https://mlsanity.com/paper/2305.07774/) (2023)
- [Pansharpening by convolutional neural networks in the full resolution framework](https://mlsanity.com/paper/2111.08334/) (2021)
- [Coupling Model-Driven and Data-Driven Methods for Remote Sensing Image Restoration and Fusion](https://mlsanity.com/paper/2108.06073/) (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)
- [FusionRF: High-Fidelity Satellite Neural Radiance Fields from Multispectral and Panchromatic Acquisitions](https://mlsanity.com/paper/2409.15132/) (2024)
- [Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information](https://mlsanity.com/paper/2103.05946/) (2021)
- [An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images](https://mlsanity.com/paper/2109.00400/) (2021)
- [LDP-Net: An Unsupervised Pansharpening Network Based on Learnable Degradation Processes](https://mlsanity.com/paper/2111.12483/) (2021)
- [SDRCNN: A single-scale dense residual connected convolutional neural network for pansharpening](https://mlsanity.com/paper/2307.00327/) (2023)
- [Learning deep multiresolution representations for pansharpening](https://mlsanity.com/paper/2102.08423/) (2021)
- [PC-GANs: Progressive Compensation Generative Adversarial Networks for Pan-sharpening](https://mlsanity.com/paper/2207.14451/) (2022)
- [Learning to Pan-sharpening with Memories of Spatial Details](https://mlsanity.com/paper/2306.16181/) (2023)
- [Understanding Pan-Sharpening via Generalized Inverse](https://mlsanity.com/paper/2310.02718/) (2023)
- [SatFusion: A Unified Framework for Enhancing Remote Sensing Images via Multi-Frame and Multi-Source Images Fusion](https://mlsanity.com/paper/2510.07905/) (2025)
- [Dual Modality Prompted Diffusion Priors for Zero Shot Hyperspectral Pansharpening](https://mlsanity.com/paper/2608.11748/) (2026)

## Abstract (arXiv)

In the field of fusing multi-spectral and panchromatic images (Pan-sharpening), the impressive effectiveness of deep neural networks has been recently employed to overcome the drawbacks of traditional linear models and boost the fusing accuracy. However, to the best of our knowledge, existing research works are mainly based on simple and flat networks with relatively shallow architecture, which severely limited their performances. In this paper, the concept of residual learning has been introduced to form a very deep convolutional neural network to make a full use of the high non-linearity of deep learning models. By both quantitative and visual assessments on a large number of high quality multi-spectral images from various sources, it has been supported that our proposed model is superior to all mainstream algorithms included in the comparison, and achieved the highest spatial-spectral unified accuracy.