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Sensors 2017, 17(10), 2196; https://doi.org/10.3390/s17102196

Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery

†,‡,* , †,‡
and
†,‡
Robotics and autonomous systems, Queensland University of Technology (QUT), Brisbane City QLD 4000, Australia
Current address: 2 George Street, Brisbane City, QLD, 4000, Australia.
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Received: 7 August 2017 / Revised: 14 September 2017 / Accepted: 21 September 2017 / Published: 24 September 2017
(This article belongs to the Special Issue UAV or Drones for Remote Sensing Applications)
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Abstract

The increased technological developments in Unmanned Aerial Vehicles (UAVs) combined with artificial intelligence and Machine Learning (ML) approaches have opened the possibility of remote sensing of extensive areas of arid lands. In this paper, a novel approach towards the detection of termite mounds with the use of a UAV, hyperspectral imagery, ML and digital image processing is intended. A new pipeline process is proposed to detect termite mounds automatically and to reduce, consequently, detection times. For the classification stage, several ML classification algorithms’ outcomes were studied, selecting support vector machines as the best approach for their role in image classification of pre-existing termite mounds. Various test conditions were applied to the proposed algorithm, obtaining an overall accuracy of 68%. Images with satisfactory mound detection proved that the method is “resolution-dependent”. These mounds were detected regardless of their rotation and position in the aerial image. However, image distortion reduced the number of detected mounds due to the inclusion of a shape analysis method in the object detection phase, and image resolution is still determinant to obtain accurate results. Hyperspectral imagery demonstrated better capabilities to classify a huge set of materials than implementing traditional segmentation methods on RGB images only. View Full-Text
Keywords: pre-existing termite mounds; UAV; hyperspectral camera; machine learning; image segmentation; support vector machines pre-existing termite mounds; UAV; hyperspectral camera; machine learning; image segmentation; support vector machines
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Sandino, J.; Wooler, A.; Gonzalez, F. Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery. Sensors 2017, 17, 2196.

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