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Remote sensing tree classification with a multilayer perceptron
To accelerate scientific progress on remote tree classification—as well as biodiversity and ecology sampling—The National Institute of Science and Technology created a community-based competition where scientists were invited to contribute informatics methods for classifying tree species and genus u...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397751/ https://www.ncbi.nlm.nih.gov/pubmed/30842894 http://dx.doi.org/10.7717/peerj.6101 |
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author | Sumsion, G Rex Bradshaw, Michael S. Hill, Kimball T. Pinto, Lucas D.G. Piccolo, Stephen R. |
author_facet | Sumsion, G Rex Bradshaw, Michael S. Hill, Kimball T. Pinto, Lucas D.G. Piccolo, Stephen R. |
author_sort | Sumsion, G Rex |
collection | PubMed |
description | To accelerate scientific progress on remote tree classification—as well as biodiversity and ecology sampling—The National Institute of Science and Technology created a community-based competition where scientists were invited to contribute informatics methods for classifying tree species and genus using crown-level images of trees. We classified tree species and genus at the pixel level using hyperspectral and LiDAR observations. We compared three algorithms that have been implemented extensively across a broad range of research applications: support vector machines, random forests, and multilayer perceptron. At the pixel level, the multilayer perceptron algorithm classified species or genus with high accuracy (92.7% and 95.9%, respectively) on the training data and performed better than the other two algorithms (85.8–93.5%). This indicates promise for the use of the multilayer perceptron (MLP) algorithm for tree-species classification based on hyperspectral and LiDAR observations and coincides with a growing body of research in which neural network-based algorithms outperform other types of classification algorithm for machine vision. To aggregate patterns across the images, we used an ensemble approach that averages the pixel-level outputs of the MLP algorithm to classify species at the crown level. The average accuracy of these classifications on the test set was 68.8% for the nine species. |
format | Online Article Text |
id | pubmed-6397751 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-63977512019-03-06 Remote sensing tree classification with a multilayer perceptron Sumsion, G Rex Bradshaw, Michael S. Hill, Kimball T. Pinto, Lucas D.G. Piccolo, Stephen R. PeerJ Ecology To accelerate scientific progress on remote tree classification—as well as biodiversity and ecology sampling—The National Institute of Science and Technology created a community-based competition where scientists were invited to contribute informatics methods for classifying tree species and genus using crown-level images of trees. We classified tree species and genus at the pixel level using hyperspectral and LiDAR observations. We compared three algorithms that have been implemented extensively across a broad range of research applications: support vector machines, random forests, and multilayer perceptron. At the pixel level, the multilayer perceptron algorithm classified species or genus with high accuracy (92.7% and 95.9%, respectively) on the training data and performed better than the other two algorithms (85.8–93.5%). This indicates promise for the use of the multilayer perceptron (MLP) algorithm for tree-species classification based on hyperspectral and LiDAR observations and coincides with a growing body of research in which neural network-based algorithms outperform other types of classification algorithm for machine vision. To aggregate patterns across the images, we used an ensemble approach that averages the pixel-level outputs of the MLP algorithm to classify species at the crown level. The average accuracy of these classifications on the test set was 68.8% for the nine species. PeerJ Inc. 2019-02-28 /pmc/articles/PMC6397751/ /pubmed/30842894 http://dx.doi.org/10.7717/peerj.6101 Text en ©2019 Sumsion et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited. |
spellingShingle | Ecology Sumsion, G Rex Bradshaw, Michael S. Hill, Kimball T. Pinto, Lucas D.G. Piccolo, Stephen R. Remote sensing tree classification with a multilayer perceptron |
title | Remote sensing tree classification with a multilayer perceptron |
title_full | Remote sensing tree classification with a multilayer perceptron |
title_fullStr | Remote sensing tree classification with a multilayer perceptron |
title_full_unstemmed | Remote sensing tree classification with a multilayer perceptron |
title_short | Remote sensing tree classification with a multilayer perceptron |
title_sort | remote sensing tree classification with a multilayer perceptron |
topic | Ecology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6397751/ https://www.ncbi.nlm.nih.gov/pubmed/30842894 http://dx.doi.org/10.7717/peerj.6101 |
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