Explainable identification and mapping of trees using UAV RGB image and deep learning

Abstract The identification and mapping of trees via remotely sensed data for application in forest management is an active area of research. Previously proposed methods using airborne and hyperspectral sensors can identify tree species with high accuracy but are costly and are thus unsuitable for s...

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Autores principales:	Masanori Onishi, Takeshi Ise
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/07bac437a5ee4be3ad04a36b253be0cd
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id	oai:doaj.org-article:07bac437a5ee4be3ad04a36b253be0cd
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spelling	oai:doaj.org-article:07bac437a5ee4be3ad04a36b253be0cd2021-12-02T14:01:37ZExplainable identification and mapping of trees using UAV RGB image and deep learning10.1038/s41598-020-79653-92045-2322https://doaj.org/article/07bac437a5ee4be3ad04a36b253be0cd2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-79653-9https://doaj.org/toc/2045-2322Abstract The identification and mapping of trees via remotely sensed data for application in forest management is an active area of research. Previously proposed methods using airborne and hyperspectral sensors can identify tree species with high accuracy but are costly and are thus unsuitable for small-scale forest managers. In this work, we constructed a machine vision system for tree identification and mapping using Red–Green–Blue (RGB) image taken by an unmanned aerial vehicle (UAV) and a convolutional neural network (CNN). In this system, we first calculated the slope from the three-dimensional model obtained by the UAV, and segmented the UAV RGB photograph of the forest into several tree crown objects automatically using colour and three-dimensional information and the slope model, and lastly applied object-based CNN classification for each crown image. This system succeeded in classifying seven tree classes, including several tree species with more than 90% accuracy. The guided gradient-weighted class activation mapping (Guided Grad-CAM) showed that the CNN classified trees according to their shapes and leaf contrasts, which enhances the potential of the system for classifying individual trees with similar colours in a cost-effective manner—a useful feature for forest management.Masanori OnishiTakeshi IseNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Masanori Onishi Takeshi Ise Explainable identification and mapping of trees using UAV RGB image and deep learning
description	Abstract The identification and mapping of trees via remotely sensed data for application in forest management is an active area of research. Previously proposed methods using airborne and hyperspectral sensors can identify tree species with high accuracy but are costly and are thus unsuitable for small-scale forest managers. In this work, we constructed a machine vision system for tree identification and mapping using Red–Green–Blue (RGB) image taken by an unmanned aerial vehicle (UAV) and a convolutional neural network (CNN). In this system, we first calculated the slope from the three-dimensional model obtained by the UAV, and segmented the UAV RGB photograph of the forest into several tree crown objects automatically using colour and three-dimensional information and the slope model, and lastly applied object-based CNN classification for each crown image. This system succeeded in classifying seven tree classes, including several tree species with more than 90% accuracy. The guided gradient-weighted class activation mapping (Guided Grad-CAM) showed that the CNN classified trees according to their shapes and leaf contrasts, which enhances the potential of the system for classifying individual trees with similar colours in a cost-effective manner—a useful feature for forest management.
format	article
author	Masanori Onishi Takeshi Ise
author_facet	Masanori Onishi Takeshi Ise
author_sort	Masanori Onishi
title	Explainable identification and mapping of trees using UAV RGB image and deep learning
title_short	Explainable identification and mapping of trees using UAV RGB image and deep learning
title_full	Explainable identification and mapping of trees using UAV RGB image and deep learning
title_fullStr	Explainable identification and mapping of trees using UAV RGB image and deep learning
title_full_unstemmed	Explainable identification and mapping of trees using UAV RGB image and deep learning
title_sort	explainable identification and mapping of trees using uav rgb image and deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/07bac437a5ee4be3ad04a36b253be0cd
work_keys_str_mv	AT masanorionishi explainableidentificationandmappingoftreesusinguavrgbimageanddeeplearning AT takeshiise explainableidentificationandmappingoftreesusinguavrgbimageanddeeplearning
_version_	1718392121483853824

Explainable identification and mapping of trees using UAV RGB image and deep learning

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