# Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network

arXiv: https://arxiv.org/abs/1706.05249 · HTML: https://mlsanity.com/paper/1706.05249/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jun 2017.

## Built on by (most cited first, 14 of 14)

- [Deep Learning in Multimodal Remote Sensing Data Fusion: A Comprehensive Review](https://mlsanity.com/paper/2205.01380/) (2022)
- [HyperTransformer: A Textural and Spectral Feature Fusion Transformer for Pansharpening](https://mlsanity.com/paper/2203.02503/) (2022)
- [ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2307.14010/) (2023)
- [A Latent Encoder Coupled Generative Adversarial Network (LE-GAN) for Efficient Hyperspectral Image Super-resolution](https://mlsanity.com/paper/2111.08685/) (2021)
- [Model Inspired Autoencoder for Unsupervised Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2110.11591/) (2021)
- [Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion](https://mlsanity.com/paper/2309.01113/) (2023)
- [Hyperspectral Image Super-resolution with Deep Priors and Degradation Model Inversion](https://mlsanity.com/paper/2201.09851/) (2022)
- [Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion](https://mlsanity.com/paper/2503.07235/) (2025)
- [Enhanced Hyperspectral Image Super-Resolution via RGB Fusion and TV-TV Minimization](https://mlsanity.com/paper/2106.07066/) (2021)
- [CSAKD: Knowledge Distillation with Cross Self-Attention for Hyperspectral and Multispectral Image Fusion](https://mlsanity.com/paper/2406.19666/) (2024)
- [HyFusion: Enhanced Reception Field Transformer for Hyperspectral Image Fusion](https://mlsanity.com/paper/2501.04665/) (2025)
- [Spatiotemporal Fusion in Remote Sensing](https://mlsanity.com/paper/2107.02701/) (2021)
- [Degradation Alchemy: Self-Supervised Unknown-to-Known Transformation for Blind Hyperspectral Image Fusion](https://mlsanity.com/paper/2503.14892/) (2025)
- [Hyperspectral Super-Resolution with Inter-Image Variability via Degradation-based Low-Rank and Residual Fusion Method](https://mlsanity.com/paper/2511.15052/) (2025)

## Abstract (arXiv)

In this paper, we propose a method using a three dimensional convolutional neural network (3-D-CNN) to fuse together multispectral (MS) and hyperspectral (HS) images to obtain a high resolution hyperspectral image. Dimensionality reduction of the hyperspectral image is performed prior to fusion in order to significantly reduce the computational time and make the method more robust to noise. Experiments are performed on a data set simulated using a real hyperspectral image. The results obtained show that the proposed approach is very promising when compared to conventional methods. This is especially true when the hyperspectral image is corrupted by additive noise.