Research team
Expertise
The topic of my research is the development of advanced hyperspectral unmixing methods. My work integrates the physical machine learning methodologies. To validate the developed techniques, I regularly capture hyperspectral datasets from the mineral powder mixtures, mineral powder pastes, drill core samples, and construction materials. This topic requires expertise in distance geometry, manifold techniques, physical modeling, and numerical optimization methods.
Advanced hyperspectral image analysis for material characterization.
Abstract
A material can be uniquely characterized by its optical reflectance spectrum. Hyperspectral cameras disperse the reflected sunlight into 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. The main objective of this project is to develop advanced innovative spectral analysis methods that relate optical reflectance to material properties. I will focus on 3 particular material properties, for which a framework will be developed, validated, and applied on specific case studies: 1. mineral composition estimation, with a case study in geological mining. 2. plant leaf biochemical parameter estimation, with a case study in multi-scale forest ecological functioning 3. water content estimation, with a case study in climate change effects on built heritageResearcher(s)
- Promoter: Scheunders Paul
- Fellow: Koirala Bikram
Research team(s)
Project type(s)
- Research Project