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基于地面的遥感和机器学习,用于现场和非侵入性监测和识别历史建筑中的盐和水分

Sotiria Kogou¹, Yu Li¹, C S Cheung¹

¹School of Science and Technology, Nottingham Trent University, Nottingham NG11 8NS, U.K.

Analytical chemistry

|February 25, 2025

概括

这项研究引入了一种新的非侵入性方法,使用遥感来监测历史建筑中的水分和盐分. 这种方法有助于在现实条件下了解盐的风化和退化.

科学领域:

遗产科学遗产科学遗产科学
保护科学保护科学
材料科学是一种材料科学.

背景情况:

历史建筑面临水分和盐活动的恶化,影响其外观和结构完整性.
现有的关于盐形成和气候变化机制的实验室研究缺乏现实世界的应用性.
在现场监测对于在现实的环境条件下了解盐活动至关重要.

研究的目的:

提出一种新的,非侵入性的方法,用于在现场识别和监测建筑材料中的水分和盐分.
在大规模评估中使用补充的遥感方法.
为了弥合实验室发现和现实世界保护挑战之间的差距.

主要方法:

基于地面的远程短波红外 (SWIR) 光谱成像,用于大表面扫描.
高分辨率角成像 (45μrad) 用于绘制湿度和盐分发的地图.
基于人工神经网络的光谱聚类用于数据可视化.
远程拉曼光谱仪用于识别集群中的盐.

主要成果:

SWIR成像成功地绘制了水分和盐在各种水化状态的空间分布.
人工神经网络通过光谱聚类有效地可视化了这些分布.
远程拉曼光谱学证实了聚类区域内盐的识别.

结论:

开发的方法允许在大型建筑表面进行非侵入性,现场监测水分和盐分.
这种方法提供了有价值的数据,以了解盐的化,并为历史建筑的保护策略提供信息.
互补的遥感技术为遗产保护和材料科学研究提供了强大的工具.

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