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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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2018-09, Vol.10 (9), p.1416
Main Authors: Kwan, Chiman, Choi, Joon, Chan, Stanley, Zhou, Jin, Budavari, Bence
Format: Article
Language:English
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
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Summary: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.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs10091416