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.
Begüm Demir is a Professor and Chair of the Remote Sensing Image Analysis (RSiM) group at the Faculty of Electrical Engineering and Computer Science, Technische Universität Berlin (TU Berlin), Germany. 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. She was a recipient of a Starting Grant from the European Research Council (ERC) with the project “BigEarth-Accurate and Scalable Processing of Big Data in Earth Observation” in 2017, and the “2018 Early Career Award” presented by the IEEE Geoscience and Remote Sensing Society. She is a senior member of IEEE since 2016.
Dr. Begüm Demir is a Scientific Committee member of the Conference on Big Data from Space, Living Planet Symposium 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.
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