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Automatic Detection and Mapping of Espeletia Plants from UAV Imagery

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  • Jorge Rodriguez
  • Ce Zhang
  • Ivan Lizarazo
  • Flavio Prieto
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Publication date12/10/2021
Host publication2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
PublisherIEEE
Pages2831-2834
Number of pages4
ISBN (Electronic)9781665403696
ISBN (Print)9781665447621
<mark>Original language</mark>English

Publication series

Name2021 IEEE International Geoscience and Remote Sensing Symposium
PublisherIEEE
ISSN (Print)2153-7003

Abstract

This paper proposes an automatic method for detection and mapping of Espeletia plants from aerial images acquired by UAV drone. The proposed approach integrated a computer vision for automatic extraction of training zones and tested on three well-established machine learning algorithms to detect regions belonging to Espeletia plants. The main components of the method are: (i) data capture and preprocessing; (ii) automatic extraction of training zones; and (iii) classification procedure using machine learning algorithms. Experimental results show that the method can achieve accurate detection and mapping of Espeletia plants, with up to 98.3% accuracy.