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Drones 2018, 2(2), 16; https://doi.org/10.3390/drones2020016

Use of UAV-Borne Spectrometer for Land Cover Classification

1
Geomatics Engineering, Department of Earth & Space Science & Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada
2
Space Engineering, Department of Earth & Space Science & Engineering, Lassonde School of Engineering, York University, Toronto, ON M3J 1P3, Canada
*
Author to whom correspondence should be addressed.
Received: 20 March 2018 / Revised: 13 April 2018 / Accepted: 17 April 2018 / Published: 20 April 2018
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Abstract

Unmanned aerial vehicles (UAV) are being used for low altitude remote sensing for thematic land classification using visible light and multi-spectral sensors. The objective of this work was to investigate the use of UAV equipped with a compact spectrometer for land cover classification. The UAV platform used was a DJI Flamewheel F550 hexacopter equipped with GPS and Inertial Measurement Unit (IMU) navigation sensors, and a Raspberry Pi processor and camera module. The spectrometer used was the FLAME-NIR, a near-infrared spectrometer for hyperspectral measurements. RGB images and spectrometer data were captured simultaneously. As spectrometer data do not provide continuous terrain coverage, the locations of their ground elliptical footprints were determined from the bundle adjustment solution of the captured images. For each of the spectrometer ground ellipses, the land cover signature at the footprint location was determined to enable the characterization, identification, and classification of land cover elements. To attain a continuous land cover classification map, spatial interpolation was carried out from the irregularly distributed labeled spectrometer points. The accuracy of the classification was assessed using spatial intersection with the object-based image classification performed using the RGB images. Results show that in homogeneous land cover, like water, the accuracy of classification is 78% and in mixed classes, like grass, trees and manmade features, the average accuracy is 50%, thus, indicating the contribution of hyperspectral measurements of low altitude UAV-borne spectrometers to improve land cover classification. View Full-Text
Keywords: UAV; spectrometer; calibration; spectral exposure; classification; land cover UAV; spectrometer; calibration; spectral exposure; classification; land cover
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Natesan, S.; Armenakis, C.; Benari, G.; Lee, R. Use of UAV-Borne Spectrometer for Land Cover Classification. Drones 2018, 2, 16.

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