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Remote Sens. 2018, 10(9), 1416;

A Super-Resolution and Fusion Approach to Enhancing Hyperspectral Images

Signal Processing, Inc., Rockville, MD 20850, USA
Department of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 USA
Google Inc., Mountain View, CA 94035, USA
Author to whom correspondence should be addressed.
Received: 9 July 2018 / Revised: 26 August 2018 / Accepted: 1 September 2018 / Published: 6 September 2018
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High resolution (HR) hyperspectral (HS) images have found widespread applications in terrestrial remote sensing applications, including vegetation monitoring, military surveillance and reconnaissance, fire damage assessment, and many others. They also find applications in planetary missions such as Mars surface characterization. However, resolutions of most HS imagers are limited to tens of meters. Existing resolution enhancement techniques either require additional multispectral (MS) band images or use a panchromatic (pan) band image. The former poses hardware challenges, whereas the latter may have limited performance. In this paper, we present a new resolution enhancement algorithm for HS images that only requires an HR color image and a low resolution (LR) HS image cube. Our approach integrates two newly developed techniques: (1) A hybrid color mapping (HCM) algorithm, and (2) A Plug-and-Play algorithm for single image super-resolution. Comprehensive experiments (objective (five performance metrics), subjective (synthesized fused images in multiple spectral ranges), and pixel clustering) using real HS images and comparative studies with 20 representative algorithms in the literature were conducted to validate and evaluate the proposed method. Results demonstrated that the new algorithm is very promising. View Full-Text
Keywords: hybrid color mapping; Hyperspectral Imaging; Plug-and-Play Alternating Direction Method of Multipliers (PAP-ADMM); remote sensing; super-resolution hybrid color mapping; Hyperspectral Imaging; Plug-and-Play Alternating Direction Method of Multipliers (PAP-ADMM); remote sensing; super-resolution

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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).

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Kwan, C.; Choi, J.H.; Chan, S.H.; Zhou, J.; Budavari, B. A Super-Resolution and Fusion Approach to Enhancing Hyperspectral Images. Remote Sens. 2018, 10, 1416.

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