# Remote Sensing Image Fusion Based on Two-stream Fusion Network

arXiv: https://arxiv.org/abs/1711.02549 · HTML: https://mlsanity.com/paper/1711.02549/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Nov 2017.

## Built on by (most cited first, 25 of 25)

- [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)
- [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)
- [A Novel State Space Model with Local Enhancement and State Sharing for Image Fusion](https://mlsanity.com/paper/2404.09293/) (2024)
- [Fourier-enhanced Implicit Neural Fusion Network for Multispectral and Hyperspectral Image Fusion](https://mlsanity.com/paper/2404.15174/) (2024)
- [Hyperspectral and multispectral image fusion with arbitrary resolution through self-supervised representations](https://mlsanity.com/paper/2405.17818/) (2024)
- [DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion](https://mlsanity.com/paper/2304.04774/) (2023)
- [Unsupervised Cycle-consistent Generative Adversarial Networks for Pan-sharpening](https://mlsanity.com/paper/2109.09395/) (2021)
- [Linearly-evolved Transformer for Pan-sharpening](https://mlsanity.com/paper/2404.12804/) (2024)
- [Unsupervised Hyperspectral and Multispectral Image Blind Fusion Based on Deep Tucker Decomposition Network with Spatial-Spectral Manifold Learning](https://mlsanity.com/paper/2409.09670/) (2024)
- [Neural Shr"odinger Bridge Matching for Pansharpening](https://mlsanity.com/paper/2404.11416/) (2024)
- [LAConv: Local Adaptive Convolution for Image Fusion](https://mlsanity.com/paper/2107.11617/) (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)
- [PanBench: Towards High-Resolution and High-Performance Pansharpening](https://mlsanity.com/paper/2311.12083/) (2023)
- [AI-Driven HSI: Multimodality, Fusion, Challenges, and the Deep Learning Revolution](https://mlsanity.com/paper/2502.06894/) (2025)
- [MESSFN : a Multi-level and Enhanced Spectral-Spatial Fusion Network for Pan-sharpening](https://mlsanity.com/paper/2109.09937/) (2021)
- [Deep dual stream residual network with contextual attention for pansharpening of remote sensing images](https://mlsanity.com/paper/2207.12004/) (2022)
- [DS_FusionNet: Dynamic Dual-Stream Fusion with Bidirectional Knowledge Distillation for Plant Disease Recognition](https://mlsanity.com/paper/2504.20948/) (2025)
- [A review of advancements in low-light image enhancement using deep learning](https://mlsanity.com/paper/2505.05759/) (2025)
- [Progressive Alignment Degradation Learning for Pansharpening](https://mlsanity.com/paper/2506.20179/) (2025)
- [Euler-inspired Decoupling Neural Operator for Efficient Pansharpening](https://mlsanity.com/paper/2604.12463/) (2026)
- [Adaptive 3D Convolution for Remote Sensing Image Fusion](https://mlsanity.com/paper/2605.09455/) (2026)
- [GSPan: A Continuous Gaussian Primitive Representation for Arbitrary-Scale Pansharpening](https://mlsanity.com/paper/2606.17722/) (2026)

## Abstract (arXiv)

Remote sensing image fusion (also known as pan-sharpening) aims at generating high resolution multi-spectral (MS) image from inputs of a high spatial resolution single band panchromatic (PAN) image and a low spatial resolution multi-spectral image. Inspired by the astounding achievements of convolutional neural networks (CNNs) in a variety of computer vision tasks, in this paper, we propose a two-stream fusion network (TFNet) to address the problem of pan-sharpening. Unlike previous CNN based methods that consider pan-sharpening as a super resolution problem and perform pan-sharpening in pixel level, the proposed TFNet aims to fuse PAN and MS images in feature level and reconstruct the pan-sharpened image from the fused features. The TFNet mainly consists of three parts. The first part is comprised of two networks extracting features from PAN and MS images, respectively. The subsequent network fuses them together to form compact features that represent both spatial and spectral information of PAN and MS images, simultaneously. Finally, the desired high spatial resolution MS image is recovered from the fused features through an image reconstruction network. Experiments on Quickbird and mboxGaoFen-1 satellite images demonstrate that the proposed TFNet can fuse PAN and MS images, effectively, and produce pan-sharpened images competitive with even superior to state of the arts.