The digital Earth Observation Librarian: a data mining approach for large satellite images archives

Abstract

Throughout the years, various Earth Observation (EO) satellites have generated huge amounts of data. The extraction of latent information in the data repositories is not a trivial task. New methodologies and tools, being capable of handling the size, complexity and variety of data, are required. Data scientists require support for the data manipulation, labeling and information extraction processes. This paper presents our Earth Observation Image Librarian (EOLib), a modular software framework which offers innovative image data mining capabilities for TerraSAR-X and EO image data, in general. The main goal of EOLib is to reduce the time needed to bring information to end-users from Payload Ground Segments (PGS). EOLib is composed of several modules which offer functionalities such as data ingestion, feature extraction from SAR (Synthetic Aperture Radar) data, meta-data extraction, semantic definition of the image content through machine learning and data mining methods, advanced querying of the image archives based on content, meta-data and semantic categories, as well as 3-D visualization of the processed images. EOLib is operated by DLR's (German Aerospace Center's) Multi-Mission Payload Ground Segment of its Remote Sensing Data Center at Oberpfaffenhofen, Germany.

An Active Learning Tool for the Generation of Earth Observation Image Benchmarks

“…3 shows the active learning-based EO benchmark tool and its module interactions. This tool evolved from EOLib [8], where the algorithms have been upgraded for Sentinel-1 and Sentinel-2 products and contain most of the innovative functionality of [6].…”

Understanding satellite images: a data mining module for Sentinel images

“…For example in (Datcu et al, 2020), for the "Water bodies" category, we are using about 12% from the entire amount of patches (at the first grid/level), while the rest of the patches are assigned to other categories and discarded from the classification. These patches are split again (in the second grid), classified, and the residues that do not belong to the desired category are removed (we keep 65% of all patches).…”