Abstract
The main goal of this project is to develop a new data analysis approach for quantitative material characterization from spectral reflectance. If successful, the proposed approach will enable accurate estimation of material properties, in a non-destructive and non-contact manner, unlike many existing laboratory-based measurement techniques. Moreover, it is employable in situ, for the inspection of large infrastructures or quality control in production. The proposed approach combines a geometric description in which material properties are represented on curves or low-dimensional surfaces, and machine learning to relate these representations to actual estimates of material properties. The representation is made invariant to spectral variability, to make it applicable under variable environmental and acquisition conditions and in cross-sensor situations. The proposed methodology will be developed for a large group of material characterization tasks. More specifically, methods are developed for estimating the material composition (i.e., the mass fractions of the material components), detecting target material components, and estimating water content. Furthermore, three use cases of the developed methodologies will be elaborated, related to corrosion monitoring and characterization of building materials.
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