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Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance
Near infrared (NIR) remote sensing has applications in vegetation analysis as well as geological investigations. For extra-terrestrial applications, this is particularly relevant to Moon, Mars and asteroid exploration, where minerals exhibiting spectral phenomenology between 600 and 800 nm have been...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271520/ https://www.ncbi.nlm.nih.gov/pubmed/34199026 http://dx.doi.org/10.3390/s21134398 |
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author | Hobbs, Steven Lambert, Andrew Ryan, Michael J. Paull, David J. Haythorpe, John |
author_facet | Hobbs, Steven Lambert, Andrew Ryan, Michael J. Paull, David J. Haythorpe, John |
author_sort | Hobbs, Steven |
collection | PubMed |
description | Near infrared (NIR) remote sensing has applications in vegetation analysis as well as geological investigations. For extra-terrestrial applications, this is particularly relevant to Moon, Mars and asteroid exploration, where minerals exhibiting spectral phenomenology between 600 and 800 nm have been identified. Recent progress in the availability of processors and sensors has created the possibility of development of low-cost instruments able to return useful scientific results. In this work, two Raspberry Pi camera types and a panchromatic astronomy camera were trialed within a pushbroom sensor to determine their utility in measuring and processing the spectrum in reflectance. Algorithmic classification of all 15 test materials exhibiting spectral phenomenology between 600 and 800 nm was easily performed. Calibration against a spectrometer considers the effects of the sensor, inherent image processing pipeline and compression. It was found that even the color Raspberry Pi cameras that are popular with STEM applications were able to record and distinguish between most minerals and, contrary to expectations, exploited the infra-red secondary transmissions in the Bayer filter to gain a wider spectral range. Such a camera without a Bayer filter can markedly improve spectral sensitivity but may not be necessary. |
format | Online Article Text |
id | pubmed-8271520 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82715202021-07-11 Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance Hobbs, Steven Lambert, Andrew Ryan, Michael J. Paull, David J. Haythorpe, John Sensors (Basel) Article Near infrared (NIR) remote sensing has applications in vegetation analysis as well as geological investigations. For extra-terrestrial applications, this is particularly relevant to Moon, Mars and asteroid exploration, where minerals exhibiting spectral phenomenology between 600 and 800 nm have been identified. Recent progress in the availability of processors and sensors has created the possibility of development of low-cost instruments able to return useful scientific results. In this work, two Raspberry Pi camera types and a panchromatic astronomy camera were trialed within a pushbroom sensor to determine their utility in measuring and processing the spectrum in reflectance. Algorithmic classification of all 15 test materials exhibiting spectral phenomenology between 600 and 800 nm was easily performed. Calibration against a spectrometer considers the effects of the sensor, inherent image processing pipeline and compression. It was found that even the color Raspberry Pi cameras that are popular with STEM applications were able to record and distinguish between most minerals and, contrary to expectations, exploited the infra-red secondary transmissions in the Bayer filter to gain a wider spectral range. Such a camera without a Bayer filter can markedly improve spectral sensitivity but may not be necessary. MDPI 2021-06-27 /pmc/articles/PMC8271520/ /pubmed/34199026 http://dx.doi.org/10.3390/s21134398 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Hobbs, Steven Lambert, Andrew Ryan, Michael J. Paull, David J. Haythorpe, John Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance |
title | Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance |
title_full | Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance |
title_fullStr | Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance |
title_full_unstemmed | Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance |
title_short | Appraisal of Low-Cost Pushbroom Hyper-Spectral Sensor Systems for Material Classification in Reflectance |
title_sort | appraisal of low-cost pushbroom hyper-spectral sensor systems for material classification in reflectance |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271520/ https://www.ncbi.nlm.nih.gov/pubmed/34199026 http://dx.doi.org/10.3390/s21134398 |
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