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Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion
This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial l...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346318/ https://www.ncbi.nlm.nih.gov/pubmed/37447978 http://dx.doi.org/10.3390/s23136128 |
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author | Wang, Hongpeng Yan, Xinru Xin, Yingjian Fang, Peipei Wang, Yian Liu, Sicong Jia, Jianjun Zhang, Liang Wan, Xiong |
author_facet | Wang, Hongpeng Yan, Xinru Xin, Yingjian Fang, Peipei Wang, Yian Liu, Sicong Jia, Jianjun Zhang, Liang Wan, Xiong |
author_sort | Wang, Hongpeng |
collection | PubMed |
description | This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial life. Fluorescence is the main factor that interferes with Raman spectroscopy detection, which will cause the enhancement of the Raman baseline and annihilate Raman information. The paper aims to evaluate fluorescence contained in the Raman baseline and characterize organic sedimentary structure using the Raman baseline. This study achieves spectral image fusion combined with mapping technology to obtain high spatial and spectral resolution fusion images. To clarify that the fluorescence of organic matter deposition is the main factor causing Raman baseline enhancement, 5041 Raman spectra were obtained in the scanning area of 710 μm × 710 μm, and the correlation mechanism between the gray level of the light-dark layer of the detection point and the Raman baseline was compared. The spatial distribution of carbonate minerals and organic precipitations was detected by combining mapping technology. In addition, based on the BI-IHS algorithm, the spectral image fusion of Raman fluorescence mapping and reflection micrograph, polarization micrograph, and orthogonal polarization micrograph are realized, respectively. A fusion image with high spectral resolution and high spatial resolution is obtained. The results show that the Raman baseline can be used as helpful information to characterize stromatolite organic sedimentary structure. |
format | Online Article Text |
id | pubmed-10346318 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103463182023-07-15 Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion Wang, Hongpeng Yan, Xinru Xin, Yingjian Fang, Peipei Wang, Yian Liu, Sicong Jia, Jianjun Zhang, Liang Wan, Xiong Sensors (Basel) Article This paper evaluates the potential application of Raman baselines in characterizing organic deposition. Taking the layered sediments (Stromatolite) formed by the growth of early life on the Earth as the research object, Raman spectroscopy is an essential means to detect deep-space extraterrestrial life. Fluorescence is the main factor that interferes with Raman spectroscopy detection, which will cause the enhancement of the Raman baseline and annihilate Raman information. The paper aims to evaluate fluorescence contained in the Raman baseline and characterize organic sedimentary structure using the Raman baseline. This study achieves spectral image fusion combined with mapping technology to obtain high spatial and spectral resolution fusion images. To clarify that the fluorescence of organic matter deposition is the main factor causing Raman baseline enhancement, 5041 Raman spectra were obtained in the scanning area of 710 μm × 710 μm, and the correlation mechanism between the gray level of the light-dark layer of the detection point and the Raman baseline was compared. The spatial distribution of carbonate minerals and organic precipitations was detected by combining mapping technology. In addition, based on the BI-IHS algorithm, the spectral image fusion of Raman fluorescence mapping and reflection micrograph, polarization micrograph, and orthogonal polarization micrograph are realized, respectively. A fusion image with high spectral resolution and high spatial resolution is obtained. The results show that the Raman baseline can be used as helpful information to characterize stromatolite organic sedimentary structure. MDPI 2023-07-03 /pmc/articles/PMC10346318/ /pubmed/37447978 http://dx.doi.org/10.3390/s23136128 Text en © 2023 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 Wang, Hongpeng Yan, Xinru Xin, Yingjian Fang, Peipei Wang, Yian Liu, Sicong Jia, Jianjun Zhang, Liang Wan, Xiong Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion |
title | Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion |
title_full | Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion |
title_fullStr | Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion |
title_full_unstemmed | Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion |
title_short | Characterization of Stromatolite Organic Sedimentary Structure Based on Spectral Image Fusion |
title_sort | characterization of stromatolite organic sedimentary structure based on spectral image fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10346318/ https://www.ncbi.nlm.nih.gov/pubmed/37447978 http://dx.doi.org/10.3390/s23136128 |
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