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.
Advancements in Building Moisture Analysis Through the Development of a Hyperspectral Scanning System.
Abstract
This project scopes the detection of moisture in historical buildings using hyperspectral imaging technology. Moisture in buildings can originate from wind-driven rain, rising damp, flooding, leaking infrastructure and condensation and periodic changes in moisture content are the main driver for several decay mechanisms. Traditional methods for the detection of moisture, like gravimetric or electrical approaches, are typically labour intensive, invasive and have limited coverage. The development of a hyperspectral scanning system for in-situ applications will allow the detection of anomalies in large-scale structures like buildings. Such anomalies include the presence of water which results in a specific absorption range in the short wave infrared spectrum. The application will capitalize on recent advances in spectral unmixing, to estimate the moisture content of several porous media, including natural stone and brick. The developments will be validated in case studies on pilot sites. This will fundamentally change the methodology of building conservation and restoration, as a more holistic understanding can be developed from whole building images, which will result in more accurate and detailed sampling strategies.Researcher(s)
- Promoter: De Kock Tim
- Co-promoter: De Kerf Thomas
- Co-promoter: Koirala Bikram
- Fellow: Sunil John
Research team(s)
Project type(s)
- Research Project
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