Maximizing Output from Hyperspectral Data for Industrial Applications

Kerschhaggl explores recent advancements in processing hyperspectral data with a focus on the fusion of hyperspectral and multimodal data streams for high-speed edge computing, enabling rapid data reduction and extraction of key data, particularly in industrial sorting contexts. To maximize the benefits of wide-bandwidth hyperspectral data, it is crucial to develop sophisticated classification and regression algorithms.

Kerschhaggl also lays the blueprint for how machine learning-based algorithm training can enhance data acquisition and enable real-time feature extraction.

About the presenter
Matthias Kerschhaggl, Ph.D., is CTIO and owner of EVK, an Austrian based expert company for industrial imaging. He is engaged in data science and analytics, predominantly dealing with data streams stemming from sensor-based sorting and control applications used in industries such as food, chemical, mining and pharmaceuticals. He holds a doctorate in experimental physics and has more than 15 years of experience in the fields of statistical learning and data mining of datasets from various areas (astroparticle physics, integral field spectrographs, hyperspectral and inductive imaging).