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Sensors
Volume 21
Issue 2
10.3390/s21020471
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Article

Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning

by
Sarah Kentsch
1,2,*,
Mariano Cabezas
3,
Luca Tomhave
2,
Jens Groß
2,
Benjamin Burkhard
2,
Maximo Larry Lopez Caceres
1,
Katsushi Waki
4 and
Yago Diez
4,*
1
Faculty of Agriculture, Yamagata University, Tsuruoka 997-8555, Japan
2
Faculty of Natural Sciences, Leibniz Universität, 30167 Hannover, Germany
3
Brain and Mind Centre, University of Sydney, Sydney 2015, Australia
4
Faculty of Science, Yamagata University, Yamagata 990-8560, Japan
*
Authors to whom correspondence should be addressed.
Sensors 2021, 21(2), 471; https://doi.org/10.3390/s21020471
Received: 27 November 2020 / Revised: 4 January 2021 / Accepted: 7 January 2021 / Published: 11 January 2021
(This article belongs to the Special Issue Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems)
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Abstract

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. View Full-Text
Keywords: ArcGIS; big data; blueberries; deep learning; image analysis; orthomosaics; segmentation refinement; UAVs ArcGIS; big data; blueberries; deep learning; image analysis; orthomosaics; segmentation refinement; UAVs
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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
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MDPI and ACS Style

Kentsch, S.; Cabezas, M.; Tomhave, L.; Groß, J.; Burkhard, B.; Lopez Caceres, M.L.; Waki, K.; Diez, Y. Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning. Sensors 2021, 21, 471. https://doi.org/10.3390/s21020471

AMA Style

Kentsch S, Cabezas M, Tomhave L, Groß J, Burkhard B, Lopez Caceres ML, Waki K, Diez Y. Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning. Sensors. 2021; 21(2):471. https://doi.org/10.3390/s21020471

Chicago/Turabian Style

Kentsch, Sarah, Mariano Cabezas, Luca Tomhave, Jens Groß, Benjamin Burkhard, Maximo L. Lopez Caceres, Katsushi Waki, and Yago Diez. 2021. "Analysis of UAV-Acquired Wetland Orthomosaics Using GIS, Computer Vision, Computational Topology and Deep Learning" Sensors 21, no. 2: 471. https://doi.org/10.3390/s21020471

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