# Moisture Content Estimation of Porous Building Stones using Hyperspectral Imaging

## TL;DR

This paper shows how hyperspectral imaging can non-destructively estimate moisture in building stones, validated against physical measurements.

## Contribution

A validated method using hyperspectral imaging and the NRAL technique for moisture content estimation in porous stones.

## Key findings

- Hyperspectral imaging accurately estimated moisture content in six stone types.
- Moisture maps showed strong agreement with gravimetric measurements.
- Pore characteristics and grain arrangement influence moisture distribution.

## Abstract

Moisture poses a major threat to built heritage through processes such as frost damage, salt crystallization and biological growth, which accelerate stone deterioration. Stone moisture content can be determined non-destructively by spectral reflectance in the shortwave infrared (SWIR) wavelength range. To validate this, large cubic and small cylindrical samples from six stone types, Brick, Euville, Massangis, Neubrunn, Obernkirchen, and Savonnières, with different controlled moisture levels were prepared, and a comprehensive SWIR hyperspectral image dataset was created. The acquired hyperspectral images were processed using the Normalized Relative Arc Lengths (NRAL) method to generate moisture maps that illustrate the spatial distribution of water within each sample. Because moisture distribution is influenced by pore characteristics and grain arrangement, the moisture maps were validated through petrographic examination. For quantitative validation, the mean moisture content of each sample was compared with the corresponding gravimetric moisture content. The results demonstrated strong agreement between the estimated and gravimetric moisture values, with root mean square errors ranging from 1 and 2 g/g  × 100.