Guest Lecture Prof Mihai Datcu

Prof Mihai Datcu facilitated the students of UMBC with his lecture on his research on “Big Data Analytics for Earth Observation”.

ABOUT PROF DATCU:Image result for mihai datcu

Prof Mihai Datcu is a Senior Scientist and Image Analysis research group leader with the Remote Sensing Technology Institute (IMF) of DLR, Oberpfaffenhofen. Since 2011, he is also leading the Immersive Visual Information Mining research lab at the Munich Aerospace Faculty and he is the director of the Research Center for Spatial Information at UPB. His interests are in Bayesian inference, information and complexity theory, stochastic processes, model-based scene understanding, image information mining for applications in information retrieval, and understanding of high resolution SAR and optical observations. He initiated the European frame of projects for Image Information Mining (IIM) and is involved in research programs for information extraction, data mining and knowledge discovery, and data understanding with the European Space Agency (ESA), NASA, and in a variety of national and European projects. He is a member of the European Image Information Mining Coordination Group (IIMCG). He and his team have developed and are currently developing the operational IIM processor in the Payload Ground Segment systems for the German missions TerraSAR-X, TanDEM-X, and the ESA Sentinel 1 and 2. He received in 2006 the Best Paper Award,  IEEE Geoscience and Remote Sensing Society Prize, in 2008 the National Order of Merit with the rank of Knight, for outstanding international research results, awarded by the President of Romania, and in 1987 the Romanian Academy Prize Traian Vuia for the development of SAADI image analysis system and activity in image processing. He is an IEEE Fellow of Signal Processing, Computer and Geoscience and Remote Sensing societies.

ABSTRACT:

The Earth is facing unprecedented climatic, geomorphologic, environmental and anthropogenic changes, which require global scale observation and monitoring, thus resulting in a multitude of new orbital and suborbital Earth Observation (EO) sensors. In particular, the Very High Resolution Satellite images are providing a wealth of information in spatial, multi-temporal, and physical parameters. However, due to their inherent complexity and also very large volumes, their automatic analysis and information extraction needs to be further evolved. Since their instrument nature, image processing, or computer vision methods are not always suitable, new methods need to be developed. The presentation aims to enlarge the preoccupation of the image processing/computer vision towards the particular challenges of VHR EO information extraction, particularly to image understanding and information mining, and focuses on the Latent Dirichlet Allocation (LDA) for spatial analysis of satellite images and the K-L similarity for image probability distribution. 

Lecture Details:

Date: Tuesday, September 20, 2016

Time: 3:00 PM

Location: ITE 325B

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