In this letter, we propose a method using a 3-D convolutional neural network to fuse together multispectral and hyperspectral (HS) images to obtain a high resolution HS image. Dimensionality reduction of the HS 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 HS image. The results obtained show that the proposed approach is very promising when compared with conventional methods. This is especially true when the HS image is corrupted by additive noise.

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method high resolution hs 3d convolutional neural network fusion dimensionality reduction multispectral and hyperspectral hs images methods approach real hs image additive noise

Frosti Palsson, Johannes R. Sveinsson, Magnus O. Ulfarsson,.Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network. 14 (5),639-643.

Frosti Palsson, Johannes R. Sveinsson, Magnus O. Ulfarsson,"Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network" 14

Frosti Palsson, Johannes R. Sveinsson, Magnus O. Ulfarsson,"Multispectral and Hyperspectral Image Fusion Using a 3-D-Convolutional Neural Network"