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Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery
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 moun...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677442/ https://www.ncbi.nlm.nih.gov/pubmed/28946639 http://dx.doi.org/10.3390/s17102196 |
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author | Sandino, Juan Wooler, Adam Gonzalez, Felipe |
author_facet | Sandino, Juan Wooler, Adam Gonzalez, Felipe |
author_sort | Sandino, Juan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5677442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-56774422017-11-17 Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery Sandino, Juan Wooler, Adam Gonzalez, Felipe Sensors (Basel) Article 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. MDPI 2017-09-24 /pmc/articles/PMC5677442/ /pubmed/28946639 http://dx.doi.org/10.3390/s17102196 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sandino, Juan Wooler, Adam Gonzalez, Felipe Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery |
title | Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery |
title_full | Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery |
title_fullStr | Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery |
title_full_unstemmed | Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery |
title_short | Towards the Automatic Detection of Pre-Existing Termite Mounds through UAS and Hyperspectral Imagery |
title_sort | towards the automatic detection of pre-existing termite mounds through uas and hyperspectral imagery |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5677442/ https://www.ncbi.nlm.nih.gov/pubmed/28946639 http://dx.doi.org/10.3390/s17102196 |
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