Next Article in Journal
Estimating Daily Global Evapotranspiration Using Penman–Monteith Equation and Remotely Sensed Land Surface Temperature
Next Article in Special Issue
Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization
Previous Article in Journal
Satellite-Derived Spatiotemporal Variations in Evapotranspiration over Northeast China during 1982–2010
Previous Article in Special Issue
Spatial Resolution Enhancement of Hyperspectral Images Using Spectral Unmixing and Bayesian Sparse Representation
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Remote Sens. 2017, 9(11), 1139;

Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network

School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710129, China
Department of Electrical and Computer Engineering, Mississippi State University, Starkville, MS 39762, USA
Author to whom correspondence should be addressed.
Received: 25 September 2017 / Revised: 22 October 2017 / Accepted: 3 November 2017 / Published: 7 November 2017
(This article belongs to the Special Issue Spatial Enhancement of Hyperspectral Data and Applications)
Full-Text   |   PDF [22314 KB, uploaded 7 November 2017]   |  


Hyperspectral images are well-known for their fine spectral resolution to discriminate different materials. However, their spatial resolution is relatively low due to the trade-off in imaging sensor technologies, resulting in limitations in their applications. Inspired by recent achievements in convolutional neural network (CNN) based super-resolution (SR) for natural images, a novel three-dimensional full CNN (3D-FCNN) is constructed for spatial SR of hyperspectral images in this paper. Specifically, 3D convolution is used to exploit both the spatial context of neighboring pixels and spectral correlation of neighboring bands, such that spectral distortion when directly applying traditional CNN based SR algorithms to hyperspectral images in band-wise manners is alleviated. Furthermore, a sensor-specific mode is designed for the proposed 3D-FCNN such that none of the samples from the target scene are required for training. Fine-tuning by a small number of training samples from the target scene can further improve the performance of such a sensor-specific method. Extensive experimental results on four benchmark datasets from two well-known hyperspectral sensors, namely hyperspectral digital imagery collection experiment (HYDICE) and reflective optics system imaging spectrometer (ROSIS) sensors, demonstrate that our proposed 3D-FCNN outperforms several existing SR methods by ensuring higher quality both in reconstruction and spectral fidelity. View Full-Text
Keywords: hyperspectral; super-resolution; convolutional neural network; deep learning hyperspectral; super-resolution; convolutional neural network; deep learning

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Mei, S.; Yuan, X.; Ji, J.; Zhang, Y.; Wan, S.; Du, Q. Hyperspectral Image Spatial Super-Resolution via 3D Full Convolutional Neural Network. Remote Sens. 2017, 9, 1139.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top