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Translational Imaging Spectroscopy for Proximal Sensing

Proximal sensing as the near field counterpart of remote sensing offers a broad variety of applications. Imaging spectroscopy in general and translational laboratory imaging spectroscopy in particular can be utilized for a variety of different research topics. Geoscientific applications require a pr...

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Autores principales: Rogass, Christian, Koerting, Friederike M., Mielke, Christian, Brell, Maximilian, Boesche, Nina K., Bade, Maria, Hohmann, Christian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579726/
https://www.ncbi.nlm.nih.gov/pubmed/28800111
http://dx.doi.org/10.3390/s17081857
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author Rogass, Christian
Koerting, Friederike M.
Mielke, Christian
Brell, Maximilian
Boesche, Nina K.
Bade, Maria
Hohmann, Christian
author_facet Rogass, Christian
Koerting, Friederike M.
Mielke, Christian
Brell, Maximilian
Boesche, Nina K.
Bade, Maria
Hohmann, Christian
author_sort Rogass, Christian
collection PubMed
description Proximal sensing as the near field counterpart of remote sensing offers a broad variety of applications. Imaging spectroscopy in general and translational laboratory imaging spectroscopy in particular can be utilized for a variety of different research topics. Geoscientific applications require a precise pre-processing of hyperspectral data cubes to retrieve at-surface reflectance in order to conduct spectral feature-based comparison of unknown sample spectra to known library spectra. A new pre-processing chain called GeoMAP-Trans for at-surface reflectance retrieval is proposed here as an analogue to other algorithms published by the team of authors. It consists of a radiometric, a geometric and a spectral module. Each module consists of several processing steps that are described in detail. The processing chain was adapted to the broadly used HySPEX VNIR/SWIR imaging spectrometer system and tested using geological mineral samples. The performance was subjectively and objectively evaluated using standard artificial image quality metrics and comparative measurements of mineral and Lambertian diffuser standards with standard field and laboratory spectrometers. The proposed algorithm provides highly qualitative results, offers broad applicability through its generic design and might be the first one of its kind to be published. A high radiometric accuracy is achieved by the incorporation of the Reduction of Miscalibration Effects (ROME) framework. The geometric accuracy is higher than 1 μpixel. The critical spectral accuracy was relatively estimated by comparing spectra of standard field spectrometers to those from HySPEX for a Lambertian diffuser. The achieved spectral accuracy is better than 0.02% for the full spectrum and better than 98% for the absorption features. It was empirically shown that point and imaging spectrometers provide different results for non-Lambertian samples due to their different sensing principles, adjacency scattering impacts on the signal and anisotropic surface reflection properties.
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spelling pubmed-55797262017-09-06 Translational Imaging Spectroscopy for Proximal Sensing Rogass, Christian Koerting, Friederike M. Mielke, Christian Brell, Maximilian Boesche, Nina K. Bade, Maria Hohmann, Christian Sensors (Basel) Article Proximal sensing as the near field counterpart of remote sensing offers a broad variety of applications. Imaging spectroscopy in general and translational laboratory imaging spectroscopy in particular can be utilized for a variety of different research topics. Geoscientific applications require a precise pre-processing of hyperspectral data cubes to retrieve at-surface reflectance in order to conduct spectral feature-based comparison of unknown sample spectra to known library spectra. A new pre-processing chain called GeoMAP-Trans for at-surface reflectance retrieval is proposed here as an analogue to other algorithms published by the team of authors. It consists of a radiometric, a geometric and a spectral module. Each module consists of several processing steps that are described in detail. The processing chain was adapted to the broadly used HySPEX VNIR/SWIR imaging spectrometer system and tested using geological mineral samples. The performance was subjectively and objectively evaluated using standard artificial image quality metrics and comparative measurements of mineral and Lambertian diffuser standards with standard field and laboratory spectrometers. The proposed algorithm provides highly qualitative results, offers broad applicability through its generic design and might be the first one of its kind to be published. A high radiometric accuracy is achieved by the incorporation of the Reduction of Miscalibration Effects (ROME) framework. The geometric accuracy is higher than 1 μpixel. The critical spectral accuracy was relatively estimated by comparing spectra of standard field spectrometers to those from HySPEX for a Lambertian diffuser. The achieved spectral accuracy is better than 0.02% for the full spectrum and better than 98% for the absorption features. It was empirically shown that point and imaging spectrometers provide different results for non-Lambertian samples due to their different sensing principles, adjacency scattering impacts on the signal and anisotropic surface reflection properties. MDPI 2017-08-11 /pmc/articles/PMC5579726/ /pubmed/28800111 http://dx.doi.org/10.3390/s17081857 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Rogass, Christian
Koerting, Friederike M.
Mielke, Christian
Brell, Maximilian
Boesche, Nina K.
Bade, Maria
Hohmann, Christian
Translational Imaging Spectroscopy for Proximal Sensing
title Translational Imaging Spectroscopy for Proximal Sensing
title_full Translational Imaging Spectroscopy for Proximal Sensing
title_fullStr Translational Imaging Spectroscopy for Proximal Sensing
title_full_unstemmed Translational Imaging Spectroscopy for Proximal Sensing
title_short Translational Imaging Spectroscopy for Proximal Sensing
title_sort translational imaging spectroscopy for proximal sensing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5579726/
https://www.ncbi.nlm.nih.gov/pubmed/28800111
http://dx.doi.org/10.3390/s17081857
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