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Proceedings Paper

Preprocessing and compression of Hyperspectral images captured onboard UAVs
Author(s): Rolando Herrero; Martin Cadirola; Vinay K. Ingle
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Paper Abstract

Advancements in image sensors and signal processing have led to the successful development of lightweight hyperspectral imaging systems that are critical to the deployment of Photometry and Remote Sensing (PaRS) capabilities in unmanned aerial vehicles (UAVs). In general, hyperspectral data cubes include a few dozens of spectral bands that are extremely useful for remote sensing applications that range from detection of land vegetation to monitoring of atmospheric products derived from the processing of lower level radiance images. Because these data cubes are captured in the challenging environment of UAVs, where resources are limited, source encoding by means of compression is a fundamental mechanism that considerably improves the overall system performance and reliability. In this paper, we focus on the hyperspectral images captured by a state-of-the-art commercial hyperspectral camera by showing the results of applying ultraspectral data compression to the obtained data set. Specifically the compression scheme that we introduce integrates two stages; (1) preprocessing and (2) compression itself. The outcomes of this procedure are linear prediction coefficients and an error signal that, when encoded, results in a compressed version of the original image. Second, preprocessing and compression algorithms are optimized and have their time complexity analyzed to guarantee their successful deployment using low power ARM based embedded processors in the context of UAVs. Lastly, we compare the proposed architecture against other well known schemes and show how the compression scheme presented in this paper outperforms all of them by providing substantial improvement and delivering both lower compression rates and lower distortion.

Paper Details

Date Published: 13 October 2015
PDF: 9 pages
Proc. SPIE 9647, Unmanned/Unattended Sensors and Sensor Networks XI; and Advanced Free-Space Optical Communication Techniques and Applications, 964705 (13 October 2015); doi: 10.1117/12.2186169
Show Author Affiliations
Rolando Herrero, Ecotronics Ventures, LLC (United States)
Martin Cadirola, Ecotronics Ventures, LLC (United States)
Vinay K. Ingle, Northeastern Univ. (United States)

Published in SPIE Proceedings Vol. 9647:
Unmanned/Unattended Sensors and Sensor Networks XI; and Advanced Free-Space Optical Communication Techniques and Applications
Edward M. Carapezza; Leslie Laycock; Henry J. White; Panos G. Datskos; Christos Tsamis, Editor(s)

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