Image and Signal Processing Methods

Hyperspectral image super-resolution: a hybrid color mapping approach

[+] Author Affiliations
Jin Zhou

Google Inc., 1600 Amphitheatre Parkway, Mountain View, California 94043, United States

Chiman Kwan, Bence Budavari

Signal Processing, Inc., 9605 Medical Center Drive, Rockville, Maryland, United States

J. Appl. Remote Sens. 10(3), 035024 (Sep 23, 2016). doi:10.1117/1.JRS.10.035024
History: Received May 2, 2016; Accepted September 1, 2016
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Abstract.  NASA has been planning a hyperspectral infrared imager mission which will provide global coverage using a hyperspectral imager with 60-m resolution. In some practical applications, such as special crop monitoring or mineral mapping, 60-m resolution may still be too coarse. There have been many pansharpening algorithms for hyperspectral images by fusing high-resolution (HR) panchromatic or multispectral images with low-resolution (LR) hyperspectral images. We propose an approach to generating HR hyperspectral images by fusing high spatial resolution color images with low spatial resolution hyperspectral images. The idea is called hybrid color mapping (HCM) and involves a mapping between a high spatial resolution color image and a low spatial resolution hyperspectral image. Several variants of the color mapping idea, including global, local, and hybrid, are proposed and investigated. It was found that the local HCM yielded the best performance. Comparison of the local HCM with >10 state-of-the-art algorithms using five performance metrics has been carried out using actual images from the air force and NASA. Although our HCM method does not require a point spread function (PSF), our results are comparable to or better than those methods that do require PSF. More importantly, our performance is better than most if not all methods that do not require PSF. After applying our HCM algorithm, not only the visual performance of the hyperspectral image has been significantly improved, but the target classification performance has also been improved. Another advantage of our technique is that it is very efficient and can be easily parallelized. Hence, our algorithm is very suitable for real-time applications.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Jin Zhou ; Chiman Kwan and Bence Budavari
"Hyperspectral image super-resolution: a hybrid color mapping approach", J. Appl. Remote Sens. 10(3), 035024 (Sep 23, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.035024


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