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Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications

Proximal remote sensing is being used across a very wide range of research fields and by scientists, who are often without deep theoretical knowledge optical physics; the author of this article falls squarely in that category! This article highlights two optical phenomena, which may greatly influenc...

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Autor principal: Nansen, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6177154/
https://www.ncbi.nlm.nih.gov/pubmed/30300357
http://dx.doi.org/10.1371/journal.pone.0204579
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author Nansen, Christian
author_facet Nansen, Christian
author_sort Nansen, Christian
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description Proximal remote sensing is being used across a very wide range of research fields and by scientists, who are often without deep theoretical knowledge optical physics; the author of this article falls squarely in that category! This article highlights two optical phenomena, which may greatly influence the quality and robustness of proximal remote sensing: penetration and scattering. Penetration implies that acquired reflectance signals are associated with both physical and chemical properties of target objects from both the surface and internal tissues/structures. Scattering implies that reflectance signals acquired from one point or object are influenced by scattered radiometric energy from neighboring points or objects. Based on a series of laboratory experiments, penetration and scattering were discussed in the context of “robustness” (repeatability) of hyperspectral reflectance data. High robustness implies that it is possible to control imaging conditions and therefore: 1) obtain very similar radiometric signals from inert objects (objects that do not change) over time, and 2) be able to consistently distinguish objects that are otherwise highly similar in appearance (size, shape, and color) and in terms of biochemical composition. It was demonstrated that robustness of hyperspectral reflectance data (40 spectral bands from 385 to 1024 nm) were significantly influenced by penetration and scattering of radiometric energy. In addition, it was demonstrated that the influence of penetration and scattering varied across the examined spectrum. Characterization of how optical phenomena may affect the robustness of reflectance data is important when using proximal remote sensing technologies as tools used to classify engineering and biological objects.
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spelling pubmed-61771542018-10-19 Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications Nansen, Christian PLoS One Research Article Proximal remote sensing is being used across a very wide range of research fields and by scientists, who are often without deep theoretical knowledge optical physics; the author of this article falls squarely in that category! This article highlights two optical phenomena, which may greatly influence the quality and robustness of proximal remote sensing: penetration and scattering. Penetration implies that acquired reflectance signals are associated with both physical and chemical properties of target objects from both the surface and internal tissues/structures. Scattering implies that reflectance signals acquired from one point or object are influenced by scattered radiometric energy from neighboring points or objects. Based on a series of laboratory experiments, penetration and scattering were discussed in the context of “robustness” (repeatability) of hyperspectral reflectance data. High robustness implies that it is possible to control imaging conditions and therefore: 1) obtain very similar radiometric signals from inert objects (objects that do not change) over time, and 2) be able to consistently distinguish objects that are otherwise highly similar in appearance (size, shape, and color) and in terms of biochemical composition. It was demonstrated that robustness of hyperspectral reflectance data (40 spectral bands from 385 to 1024 nm) were significantly influenced by penetration and scattering of radiometric energy. In addition, it was demonstrated that the influence of penetration and scattering varied across the examined spectrum. Characterization of how optical phenomena may affect the robustness of reflectance data is important when using proximal remote sensing technologies as tools used to classify engineering and biological objects. Public Library of Science 2018-10-09 /pmc/articles/PMC6177154/ /pubmed/30300357 http://dx.doi.org/10.1371/journal.pone.0204579 Text en © 2018 Christian Nansen 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, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Nansen, Christian
Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications
title Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications
title_full Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications
title_fullStr Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications
title_full_unstemmed Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications
title_short Penetration and scattering—Two optical phenomena to consider when applying proximal remote sensing technologies to object classifications
title_sort penetration and scattering—two optical phenomena to consider when applying proximal remote sensing technologies to object classifications
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6177154/
https://www.ncbi.nlm.nih.gov/pubmed/30300357
http://dx.doi.org/10.1371/journal.pone.0204579
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