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

• Published in: IEEE geoscience and remote sensing letters Vol. 14; no. 5; pp. 639 - 643
• Main Authors: Palsson, Frosti, Sveinsson, Johannes R., Ulfarsson, Magnus O.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01-05-2017; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Additives; Computer applications; Convolutional neural networks (CNNs); deep learning (DL); hyperspectral (HS); Hyperspectral imaging; image fusion; Image resolution; Loading; Methods; multispectral (MS); Multispectral analysis; Neural networks; Neurons; Noise reduction; Principal component analysis; Remote sensing; Spatial resolution; Training
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2017.2668299

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.