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Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning
Invasive blueberry species endanger the sensitive environment of wetlands and protection laws call for management measures. Therefore, methods are needed to identify blueberry bushes, locate them, and characterise their distribution and properties with a minimum of disturbance. UAVs (Unmanned Aerial...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827223/ https://www.ncbi.nlm.nih.gov/pubmed/33440797 http://dx.doi.org/10.3390/s21020471 |
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author | Kentsch, Sarah Cabezas, Mariano Tomhave, Luca Groß, Jens Burkhard, Benjamin Lopez Caceres, Maximo Larry Waki, Katsushi Diez, Yago |
author_facet | Kentsch, Sarah Cabezas, Mariano Tomhave, Luca Groß, Jens Burkhard, Benjamin Lopez Caceres, Maximo Larry Waki, Katsushi Diez, Yago |
author_sort | Kentsch, Sarah |
collection | PubMed |
description | Invasive blueberry species endanger the sensitive environment of wetlands and protection laws call for management measures. Therefore, methods are needed to identify blueberry bushes, locate them, and characterise their distribution and properties with a minimum of disturbance. UAVs (Unmanned Aerial Vehicles) and image analysis have become important tools for classification and detection approaches. In this study, techniques, such as GIS (Geographical Information Systems) and deep learning, were combined in order to detect invasive blueberry species in wetland environments. Images that were collected by UAV were used to produce orthomosaics, which were analysed to produce maps of blueberry location, distribution, and spread in each study site, as well as bush height and area information. Deep learning networks were used with transfer learning and unfrozen weights in order to automatically detect blueberry bushes reaching True Positive Values (TPV) of 93.83% and an Overall Accuracy (OA) of 98.83%. A refinement of the result masks reached a Dice of 0.624. This study provides an efficient and effective methodology to study wetlands while using different techniques. |
format | Online Article Text |
id | pubmed-7827223 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-78272232021-01-25 Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning Kentsch, Sarah Cabezas, Mariano Tomhave, Luca Groß, Jens Burkhard, Benjamin Lopez Caceres, Maximo Larry Waki, Katsushi Diez, Yago Sensors (Basel) Article Invasive blueberry species endanger the sensitive environment of wetlands and protection laws call for management measures. Therefore, methods are needed to identify blueberry bushes, locate them, and characterise their distribution and properties with a minimum of disturbance. UAVs (Unmanned Aerial Vehicles) and image analysis have become important tools for classification and detection approaches. In this study, techniques, such as GIS (Geographical Information Systems) and deep learning, were combined in order to detect invasive blueberry species in wetland environments. Images that were collected by UAV were used to produce orthomosaics, which were analysed to produce maps of blueberry location, distribution, and spread in each study site, as well as bush height and area information. Deep learning networks were used with transfer learning and unfrozen weights in order to automatically detect blueberry bushes reaching True Positive Values (TPV) of 93.83% and an Overall Accuracy (OA) of 98.83%. A refinement of the result masks reached a Dice of 0.624. This study provides an efficient and effective methodology to study wetlands while using different techniques. MDPI 2021-01-11 /pmc/articles/PMC7827223/ /pubmed/33440797 http://dx.doi.org/10.3390/s21020471 Text en © 2021 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 Kentsch, Sarah Cabezas, Mariano Tomhave, Luca Groß, Jens Burkhard, Benjamin Lopez Caceres, Maximo Larry Waki, Katsushi Diez, Yago Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning |
title | Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning |
title_full | Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning |
title_fullStr | Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning |
title_full_unstemmed | Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning |
title_short | Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning |
title_sort | analysis of uav-acquired wetland orthomosaics using gis, computer vision, computational topology and deep learning |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7827223/ https://www.ncbi.nlm.nih.gov/pubmed/33440797 http://dx.doi.org/10.3390/s21020471 |
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