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Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms

Hemispherical photography is a well-established method to optically assess ecological parameters related to plant canopies; e.g. ground-level light regimes and the distribution of foliage within the crown space. Interpreting hemispherical photographs involves classifying pixels as either sky or vege...

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Detalles Bibliográficos
Autores principales: Glatthorn, Jonas, Beckschäfer, Philip
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4242535/
https://www.ncbi.nlm.nih.gov/pubmed/25420014
http://dx.doi.org/10.1371/journal.pone.0111924
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author Glatthorn, Jonas
Beckschäfer, Philip
author_facet Glatthorn, Jonas
Beckschäfer, Philip
author_sort Glatthorn, Jonas
collection PubMed
description Hemispherical photography is a well-established method to optically assess ecological parameters related to plant canopies; e.g. ground-level light regimes and the distribution of foliage within the crown space. Interpreting hemispherical photographs involves classifying pixels as either sky or vegetation. A wide range of automatic thresholding or binarization algorithms exists to classify the photographs. The variety in methodology hampers ability to compare results across studies. To identify an optimal threshold selection method, this study assessed the accuracy of seven binarization methods implemented in software currently available for the processing of hemispherical photographs. Therefore, binarizations obtained by the algorithms were compared to reference data generated through a manual binarization of a stratified random selection of pixels. This approach was adopted from the accuracy assessment of map classifications known from remote sensing studies. Percentage correct ([Image: see text]) and kappa-statistics ([Image: see text]) were calculated. The accuracy of the algorithms was assessed for photographs taken with automatic exposure settings (auto-exposure) and photographs taken with settings which avoid overexposure (histogram-exposure). In addition, gap fraction values derived from hemispherical photographs were compared with estimates derived from the manually classified reference pixels. All tested algorithms were shown to be sensitive to overexposure. Three of the algorithms showed an accuracy which was high enough to be recommended for the processing of histogram-exposed hemispherical photographs: “Minimum” ([Image: see text] 98.8%; [Image: see text] 0.952), “Edge Detection” ([Image: see text] 98.1%; [Image: see text] 0.950), and “Minimum Histogram” ([Image: see text] 98.1%; [Image: see text] 0.947). The Minimum algorithm overestimated gap fraction least of all (11%). The overestimation by the algorithms Edge Detection (63%) and Minimum Histogram (67%) were considerably larger. For the remaining four evaluated algorithms (IsoData, Maximum Entropy, MinError, and Otsu) an incompatibility with photographs containing overexposed pixels was detected. When applied to histogram-exposed photographs, these algorithms overestimated the gap fraction by at least 180%.
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spelling pubmed-42425352014-11-26 Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms Glatthorn, Jonas Beckschäfer, Philip PLoS One Research Article Hemispherical photography is a well-established method to optically assess ecological parameters related to plant canopies; e.g. ground-level light regimes and the distribution of foliage within the crown space. Interpreting hemispherical photographs involves classifying pixels as either sky or vegetation. A wide range of automatic thresholding or binarization algorithms exists to classify the photographs. The variety in methodology hampers ability to compare results across studies. To identify an optimal threshold selection method, this study assessed the accuracy of seven binarization methods implemented in software currently available for the processing of hemispherical photographs. Therefore, binarizations obtained by the algorithms were compared to reference data generated through a manual binarization of a stratified random selection of pixels. This approach was adopted from the accuracy assessment of map classifications known from remote sensing studies. Percentage correct ([Image: see text]) and kappa-statistics ([Image: see text]) were calculated. The accuracy of the algorithms was assessed for photographs taken with automatic exposure settings (auto-exposure) and photographs taken with settings which avoid overexposure (histogram-exposure). In addition, gap fraction values derived from hemispherical photographs were compared with estimates derived from the manually classified reference pixels. All tested algorithms were shown to be sensitive to overexposure. Three of the algorithms showed an accuracy which was high enough to be recommended for the processing of histogram-exposed hemispherical photographs: “Minimum” ([Image: see text] 98.8%; [Image: see text] 0.952), “Edge Detection” ([Image: see text] 98.1%; [Image: see text] 0.950), and “Minimum Histogram” ([Image: see text] 98.1%; [Image: see text] 0.947). The Minimum algorithm overestimated gap fraction least of all (11%). The overestimation by the algorithms Edge Detection (63%) and Minimum Histogram (67%) were considerably larger. For the remaining four evaluated algorithms (IsoData, Maximum Entropy, MinError, and Otsu) an incompatibility with photographs containing overexposed pixels was detected. When applied to histogram-exposed photographs, these algorithms overestimated the gap fraction by at least 180%. Public Library of Science 2014-11-24 /pmc/articles/PMC4242535/ /pubmed/25420014 http://dx.doi.org/10.1371/journal.pone.0111924 Text en © 2014 Glatthorn, Beckschäfer http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Glatthorn, Jonas
Beckschäfer, Philip
Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms
title Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms
title_full Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms
title_fullStr Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms
title_full_unstemmed Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms
title_short Standardizing the Protocol for Hemispherical Photographs: Accuracy Assessment of Binarization Algorithms
title_sort standardizing the protocol for hemispherical photographs: accuracy assessment of binarization algorithms
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4242535/
https://www.ncbi.nlm.nih.gov/pubmed/25420014
http://dx.doi.org/10.1371/journal.pone.0111924
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