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Project

Material inspection by shortwave infrared hyperspectral image analysis.

A widely used non-destructive method for material inspection is computer vision using RGB cameras along with matching image analysis. This allows to spatially resolve inhomogeneities in the materials. Computer vision however is limited to the visual part of the electromagnetic spectrum (400-700 nm), while many of the chemical processes and mineral formations in materials have particular reflectance properties in the near infrared (NIR, 700-1000 nm) and the shortwave infrared (SWIR, 1000-2500 nm). Spectrometers, on the other hand, are able to resolve these properties in the spectral direction but can only provide point-based measurements. The main scientific objective of this project is to exploit and enhance the potential of hyperspectral imaging in the NIR and SWIR range for the characterization of heterogeneously mixed and compound materials. For this, we will develop hyperspectral image analysis methods with increasing levels of complexity, ranging from spectral indices, characteristic for minerals and compounds in the materials, over methods based on spectral libraries, to supervised spectral unmixing methods. The developed methods will be validated on data acquired from homemade mixtures (mineral mixtures, sands, powdered clays and mortar) and implemented on two real use cases, i.e. mineral detection and corrosion detection.
Date:1 Jan 2021 →  Today
Keywords:HYPERSPECTRAL DATA ANALYSIS
Disciplines:Machine learning and decision making, Image processing, Pattern recognition and neural networks