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On the generation of high-spatial and high-spectral resolution images using THEMIS and TES for Mars exploration
Author(s): Chiman Kwan; Christopher Haberle; Bulent Ayhan; Bryan Chou; Adam Echavarren; Giorgy Castaneda; Bence Budavari; Scott Dickenshied
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Paper Abstract

In the 2015 NASA ROSES solicitation, NASA has expressed strong interest in improving the accuracy of Mars surface characterization using satellite images. Thermal Emission Imaging System (THEMIS), an imager with a spatial resolution of 100 meters, has 10 infrared bands between 6 and 15 micrometers. Thermal Emission Spectrometer (TES), an imager with a spatial resolution of 3 km, has 143 bands between 5 and 50 micrometers. While both imagers have a variety of applications, it would be ideal to generate high-spatial and high-spectral resolution data products by fusing their respective outputs. We present a novel approach to fusing THEMIS and TES satellite images, aiming to improve orbital characterization of Mars’ surface. First, the THEMIS bands must undergo atmospheric compensation (AC) due to the presence of dust, ice, carbon dioxide, etc. A systematic AC procedure using elevation information and spectrally uniform pixels has been developed and implemented. Second, a set of proven pan-sharpening algorithms has been applied to fuse the two sets of images. The pan-sharpened images have the spatial resolution of THEMIS images and the spectral resolution of TES images. The results of extensive experiments using THEMIS and TES data collected near the Syrtis Major region (one of the final 3 candidate landing sites for the Mars 2020 rover) clearly demonstrate the feasibility of the proposed approach.

Paper Details

Date Published: 8 May 2018
PDF: 13 pages
Proc. SPIE 10644, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV, 106440B (8 May 2018); doi: 10.1117/12.2303758
Show Author Affiliations
Chiman Kwan, Applied Research LLC (United States)
Christopher Haberle, Arizona State Univ. (United States)
Bulent Ayhan, Applied Research LLC (United States)
Bryan Chou, Applied Research LLC (United States)
Adam Echavarren, Applied Research LLC (United States)
Giorgy Castaneda, Applied Research LLC (United States)
Bence Budavari, Applied Research LLC (United States)
Scott Dickenshied, Arizona State Univ. (United States)


Published in SPIE Proceedings Vol. 10644:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XXIV
Miguel Velez-Reyes; David W. Messinger, Editor(s)

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