Improved Earth Observation Data Retrieval through Hashing Algorithms

Throughout the years, a wide range of satellite mission enabled the creation of a huge amount of Earth Observation (EO) data carrying complex information, whose exploitation is left behind due to the lack of handling capabilities. Computational resources are hardly keeping up with content analysis and information retrieval. In order to increase the search speed through data warehouses for knowledge discovery, new indexing methods are required to handle both the size and the informational complexity of EO data. Feature extraction algorithms are able to describe the image content, yet, they require a very complex database. In this paper, we propose a methodology that combines feature extraction, hashing methods and optimized indexing to convert the images characteristics into hash codes in an effort to speed up the search process. For our experiments, we run our procedure on a data-set composed of several Sentinel-2 acquisitions form across Europe and we assess the query times.