Research Publications

Explore hyperspectral imaging by reading our research articles


15th International Mine Water Association Congress 2024: Hyperspectral UAV-sensing for monitoring tailing ponds: Towards responsible resource repurposing

The interest in repurposing mine residues has substantially increased over the last decade as the mining industry endeavors to minimize environmental footprints and legacies. The industry also aims to meet the growing demand for critical raw materials
(CRMs), especially those essential for technology, by considering mine waste as a potential supplementary secondary resource. As part of the ongoing mine water management perpetual tasks in the Ibbenbüren coalfield in Germany (closed in 2018), residual sludge material from the Gravenhorst sewage treatment ….

Research Publications - Hyperspectral Imaging


Estimating dry matter and total soluble content in apples using a commercial portable hyperspectral imaging system

The quest for rapid, non-destructive, and precise technologies for fruit quality estimation is motivated by the needs across the whole food production chain. One of the emerging technologies fulfilling these requirements is spectral imaging. However, despite documented successes, the technology is yet to become established in commercial applications. The best results reported in the literature rely on fixed, non-portable dedicated setups, and controlled light conditions, which limits the potential use cases along the food production chain.

Research Publications - Hyperspectral Imaging


Characterization and digital aberration correction of a hyperspectral imaging system for plant disease detection

Hyperspectral imaging is a key technology for monitoring agricultural crops and vegetation. It can be used for health estimation and the early detection of disease symptoms in plants. This can help to reduce the use of pesticides by allowing targeted and early intervention. Cost-efficient hyperspectral imaging systems are necessary to meet the increasing demand for monitoring techniques for agricultural products. These systems usually suffer from sub-optimal image quality. Here we present a digital aberration correction for hyperspectral image data.

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