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

## TL;DR

This paper introduces a 3-D convolutional neural network approach to fuse multispectral and hyperspectral images, achieving high-resolution hyperspectral images with reduced noise sensitivity and computational efficiency.

## Contribution

The paper presents a novel 3-D-CNN based fusion method with dimensionality reduction, improving robustness and efficiency over traditional techniques.

## Key findings

- Outperforms conventional fusion methods in accuracy
- Effective noise robustness demonstrated
- Reduces computational time significantly

## Abstract

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.