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

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.

Locations

• IEEE Geoscience and Remote Sensing Letters - View
• arXiv (Cornell University) - View - PDF
• DataCite API - View