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A Super-Resolution and Fusion Approach to Enhancing Hyperspectral Images
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...
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Published in: | Remote sensing (Basel, Switzerland) Switzerland), 2018-09, Vol.10 (9), p.1416 |
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description | 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. |
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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.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs10091416</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Algorithms ; Bands ; Clustering ; Color ; Comparative studies ; Damage assessment ; Earth science ; Fire damage ; hybrid color mapping ; Hyperspectral Imaging ; Image enhancement ; Image processing ; Image resolution ; Mars ; Mars missions ; Mars surface ; Measuring instruments ; Medical imaging ; Methods ; Performance measurement ; Plug-and-Play Alternating Direction Method of Multipliers (PAP-ADMM) ; Principal components analysis ; Remote monitoring ; Remote sensing ; super-resolution ; Surface properties ; Ubiquitous computing</subject><ispartof>Remote sensing (Basel, Switzerland), 2018-09, Vol.10 (9), p.1416</ispartof><rights>2018. 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Bence</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Super-Resolution and Fusion Approach to Enhancing Hyperspectral Images</atitle><jtitle>Remote sensing (Basel, Switzerland)</jtitle><date>2018-09-01</date><risdate>2018</risdate><volume>10</volume><issue>9</issue><spage>1416</spage><pages>1416-</pages><issn>2072-4292</issn><eissn>2072-4292</eissn><abstract>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.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/rs10091416</doi><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Bands Clustering Color Comparative studies Damage assessment Earth science Fire damage hybrid color mapping Hyperspectral Imaging Image enhancement Image processing Image resolution Mars Mars missions Mars surface Measuring instruments Medical imaging Methods Performance measurement Plug-and-Play Alternating Direction Method of Multipliers (PAP-ADMM) Principal components analysis Remote monitoring Remote sensing super-resolution Surface properties Ubiquitous computing |
title | A Super-Resolution and Fusion Approach to Enhancing Hyperspectral Images |
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