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Proceedings Paper

UAV-based hyperspectral imaging detection for explosives and contaminants
Author(s): Yangyang Liu; Qunbo Lv; Jianwei Wang; Linlin Pei; Weiyan Li
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Paper Abstract

In order to accomplish the remote sensing detection demands of drones, which is aimed at potential hazards such as explosives and pollutants on the disaster site, the uav-based hyperspectral imaging remote sensing detection methods should be researched. Taking into account the spectral information on disaster site is frequently weak, the detection mechanism of hyperspectral imaging technology leads to its low sensitivity to weak information. The integrated multiplexing method of complex optical path, high-sensitivity hyperspectral spectroscopic imaging function, integrated design method, and the optical aberration optimization of wide band in large field of view are studied, the lightminiaturization high-sensitivity hyperspectral imager on drones has been developed, which breaks through the key technologies of high-sensitivity spectral imaging. Meanwhile, in order to ensure the timeliness of explosive information detection, the rapid and high-precision extracting processing methods of the large amount of hyperspectral data for targets such as explosives on the disaster site must be deliberated, while developing the rapid and high-precision imaging hyperspectral data processing software, to provide rapidly detailed information supporting for accident disposal and rescue deployment on the disaster site, and implement the practical application of hyperspectral imaging technology in disaster and environmental monitoring.

Paper Details

Date Published: 7 October 2019
PDF: 10 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 111551Q (7 October 2019); doi: 10.1117/12.2532716
Show Author Affiliations
Yangyang Liu, Academy of Opto-Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Qunbo Lv, Academy of Opto-Electronics (China)
Univ. of Chinese Academy of Sciences (China)
Jianwei Wang, Academy of Opto-Electronics (China)
Linlin Pei, Academy of Opto-Electronics (China)
Weiyan Li, Academy of Opto-Electronics (China)

Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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