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GERAD seminar

Unsupervised and self-taught learning for remote sensing image interpretation

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Mar 15, 2017   10:45 AM — 12:00 PM

Ribana Roscher University of Bonn, Germany

This presentation gives an overview about my current research with remote sensing application examples. In my research I am aiming at the development of pattern recognition methods, which are particularly designed for the analysis of large scale remote sensing data. I specifically focus on efficient classification methods, techniques for sophisticated feature learning and the integration of prior knowledge such as spatial and temporal information. A central idea in my research is to develop methods which ensure a high discrimination power and at the same time model the underlying structure of the data, since such methods are a prerequisite for the automatic analysis of earth observation data. More specifically, my main focus is on unsupervised and self-taught learning in order to integrate unlabeled data for the classifcation process. This covers at the moment mostly methods such as sparse representation, archetypal analysis and one-class classifier. My applications cover the analysis and interpretation of multi- and hyperspectral aerial and satellite images (LULC classification), but also the detection of unknown classes and anomaly detection.


Free entrance.
Welcome to everyone!

Andrea Lodi organizer
Vahid Partovi Nia organizer

Location

Room 4488
André-Aisenstadt Building
Université de Montréal Campus
2920, chemin de la Tour
Montréal QC H3T 1J4
Canada

Associated organization

Research Axis

Research application