  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.
