A new framework for quantitative characterization of water in materials by VNIR and SWIR optical reflectance imaging.

With technological progress, small size, low cost multispectral and hyperspectral cameras become available that capture the light in up to hundreds of consecutive small wavelength bands in the visible and near infrared (VNIR, 400-1000 nm) and the shortwave infrared (SWIR, 1000-2500 nm) regions. These cameras can be installed on unmanned aerial vehicles, agricultural equipment or conveyer belts, or used in laboratory environments and even be used as handheld devices. Numerous applications can be considered for industrial inspection and quality control. As water has very strong absorption power, in particular in the SWIR range (e.g., absorption peaks around 1400 and 1900 nm), the optical reflectance properties of water-bearing materials are dominated by water. This can be disadvantageous when characterizing materials, as the material reflectance characteristics are largely hidden because of the water absorption. On the other hand, this gives opportunities to focus on water-related properties of a material, e.g., its water content, or specific material parameters that can be related to water content, e.g., leaf physiological parameters such as the Equivalent Water Thickness. The goal of this project is to study the characterization of water-bearing materials from optical reflectance imaging and to estimate and spatially resolve the water content and other relevant water-related material parameters. The main novelty and challenge is the development of a hyperspectral image analysis framework that is • invariant to environmental and acquisition conditions, • generically applicable to a large group of materials. These improvements will allow to upscale from point-based laboratory applications on benchmark datasets to spatially resolved real world in situ applications. The developed framework will be validated on two specific use cases: moisture content estimation in soils, and plant leaf parameter estimation.

Keywords:HYPERSPECTRAL DATA ANALYSIS, HYPERSPECTRAL REMOTE SENSING

Disciplines:Data visualisation and imaging, Photogrammetry and remote sensing