Presentation:
Analysis of Big Data in Earth Observation
During the last decade, a huge number of earth observation (EO) satellites with optical and Synthetic Aperture Radar sensors onboard have been launched and advances in satellite systems have increased the amount and variety of EO data. This has led to massive EO data archives with huge amount of remote sensing (RS) images, from which retrieving useful information is challenging. In view of that, content based image retrieval (CBIR) has attracted great attention in the RS community. In this lecture, a general overview on scientific and practical problems related to RS image characterization, indexing and search from massive archives will be initially discussed. Then, recent developments that can overcome the considered problems will be introduced by focusing on semantic-sensitive hashing based scalable and accurate RS CBIR systems.
Bio
Begüm Demir is a Professor and Chair of the Remote Sensing Image Analysis (RSiM) group at the Technische Universität Berlin (TU Berlin). Before joining to TU Berlin, she was a Professor at the University of Trento, Italy from 2013 to 2018. Her research interests include image processing and machine learning with applications to remote sensing image analysis. In particular, she conducts research on remote sensing image classification, biophysical parameters estimation, and content-based remote sensing image retrieval. She was a recipient of an ERC Starting Grant with the project “BigEarth-Accurate and Scalable Processing of Big Data in Earth Observation” in 2017 and IEEE Geoscience and Remote Sensing Society Early Career Award in 2018.
Dr. Begüm Demir is a Scientific Committee member of the Conference on Big Data from Space and SPIE International Conference on Signal and Image Processing for Remote Sensing. She is the founder and the co-chair of Image and Signal Processing for Remote Sensing Workshop organized within the IEEE Conference on Signal Processing and Communications Applications since 2014. She is a senior member of IEEE since 2016.
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