A Comprehensive Compilation of Spectral Libraries for Petroleum Hydrocarbons (PHC) Encompassing VNIR-SWIR-TIR Ranges.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES Scientific Data Pub Date : 2024-10-02 DOI:10.1038/s41597-024-03892-y

Carlos Roberto de Souza Filho, Rebecca D P M Scafutto

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Abstract

Remote detection and mapping of surface materials using optical sensors relies predominantly on analyzing multispectral and hyperspectral imagery employing classification algorithms. The classification process involves comparing the spectra of individual pixels within the image to spectra from reference databases, commonly referred to as spectral libraries. Here, we introduce a comprehensive compilation of spectral libraries specifically tailored for petroleum hydrocarbons (PHC), meticulously crafted under controlled laboratory conditions. This compilation includes reference spectral libraries for various PHC forms, including crude oils, mineral substrate-PHC mixtures (comprising crude oils and fuels), oil-film on water, and oil-water emulsions. Data collection was conducted within the visible, near, and shortwave IR (VNIR-SWIR - 0.35-2.5 µm) spectra and thermal IR (TIR - 3-15 µm) range. The openly accessible spectral libraries presented herein support the scientific community and industry in characterizing field samples or spectral data from onshore and offshore sites. Furthermore, these libraries are instrumental in developing and applying classification algorithms designed for processing spectral images captured by cameras coupled to multiple platforms (e.g., tripods, drones, airborne, orbital satellites).

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石油碳氢化合物 (PHC) 光谱库综合汇编，涵盖近红外-西红外-红外光谱范围。

使用光学传感器对表面材料进行远程探测和绘图主要依赖于利用分类算法对多光谱和高光谱图像进行分析。分类过程包括将图像中各个像素的光谱与参考数据库（通常称为光谱库）中的光谱进行比较。在此，我们将介绍在受控实验室条件下精心制作的专为石油碳氢化合物 (PHC) 量身定制的光谱库综合汇编。该汇编包括各种石油烃形式的参考光谱库，包括原油、矿物基质-石油烃混合物（包括原油和燃料）、水上油膜和油水乳液。数据收集在可见光、近红外和短波红外（VNIR-SWIR - 0.35-2.5 µm）光谱以及热红外（TIR - 3-15 µm）范围内进行。本文介绍的可公开访问的光谱库可为科学界和工业界提供支持，帮助他们鉴定来自陆上和近海站点的现场样本或光谱数据。此外，这些光谱库还有助于开发和应用分类算法，这些算法专为处理与多种平台（如三脚架、无人机、机载、轨道卫星）相连接的相机拍摄的光谱图像而设计。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.