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Journal of Applied Remote Sensing

Hyperspectral image super-resolution: a hybrid color mapping approach
Author(s): Jin Zhou; Chiman Kwan; Bence Budavari
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Paper 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.

Paper Details

Date Published: 23 September 2016
PDF: 20 pages
J. Appl. Rem. Sens. 10(3) 035024 doi: 10.1117/1.JRS.10.035024
Published in: Journal of Applied Remote Sensing Volume 10, Issue 3
Show Author Affiliations
Jin Zhou, Google (United States)
Chiman Kwan, Signal Processing, Inc. (United States)
Bence Budavari, Signal Processing, Inc. (United States)

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