Hyper-spectral imaging for airborne meteorite detection

Moorhouse, David (2014) Hyper-spectral imaging for airborne meteorite detection. [USQ Project]


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Meteorites are sought after by both scientists and enthusiasts due to their unique characteristics and the window they provide to the broader universe. Current meteorite collection methods are labour and resource intensive and return only relatively few finds in the context of the investment. The basis of this project was to investigate whether meteorites can be identified through a hyper-spectral camera which would be ultimately fitted to an unmanned aerial vehicle (UAV). Such an approach
would allow greater geographic coverage of search areas, less human resources and potentially due to these factors, a greater return on investment. While work has been undertaken on identifying the spectral signatures of meteorites and on the use of hyper-spectral imaging in detection and identification, a search of the literature
reveals that no earlier work on the use of hyper-spectral imaging for the identification and detection of meteorites. This project therefore builds on the more general work undertaken to apply hyper-spectral imaging to meteorite detection and identification.

A key component of this project was the design and construction of a low cost hyper-spectral camera, which involved the development of two prototypes. Collection
of hyper-spectral data, including of meteorites and known and unknown terrestrial rocks, was performed. This was then analysed for the presence of meteorites. The analysis and interpretation of this data required the research and
development of a system to analyse the data to determine the presence and location of objects of interest. Ultimately this has produced a system that analyses
hyper-spectral data to determine the the presence of particular types of meteorites under full sun lit conditions. The software that produces these results also logs the presence of the meteorites against the frame number and location of the find.

The findings of the project indicate that hyper-spectral imaging is an appropriate way to detect and identify meteorites both at a pure spectral level and practically
with imperfect equipment that relies upon reflections of sunlight off the sample materials. The project identifies further work which would allow meteorite detection
from an aerial vehicle. While, the software which enables the meteorite detection system to perform hyper-spectral analysis and meteorite detection on board an
aerial vehicle has been written, the hardware requires further work. The hardware (that is, the hyper-spectral camera) requires refinement to support its use on an
aerial vehicle, including ensuring an appropriate level of robustness to support its use on an aerial vehicle in remote areas.

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Item Type: USQ Project
Item Status: Live Archive
Additional Information: Bachelor of Engineering project.
Faculty/School / Institute/Centre: Historic - Faculty of Health, Engineering and Sciences - School of Mechanical and Electrical Engineering (1 Jul 2013 - 31 Dec 2021)
Supervisors: Low, Tobias
Date Deposited: 09 Sep 2015 05:05
Last Modified: 03 Mar 2016 04:18
Uncontrolled Keywords: hyper-spectral imaging; meteorites; UAVs; unmanned aerial vehicle; meteorite collection methods
Fields of Research (2008): 02 Physical Sciences > 0201 Astronomical and Space Sciences > 020199 Astronomical and Space Sciences not elsewhere classified
09 Engineering > 0906 Electrical and Electronic Engineering > 090605 Photodetectors, Optical Sensors and Solar Cells
10 Technology > 1099 Other Technology > 109999 Technology not elsewhere classified
Fields of Research (2020): 51 PHYSICAL SCIENCES > 5199 Other physical sciences > 519999 Other physical sciences not elsewhere classified
40 ENGINEERING > 4009 Electronics, sensors and digital hardware > 400999 Electronics, sensors and digital hardware not elsewhere classified
31 BIOLOGICAL SCIENCES > 3106 Industrial biotechnology > 310699 Industrial biotechnology not elsewhere classified
URI: https://sear.unisq.edu.au/id/eprint/27364

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