The Earth Observation Image Librarian (EOLIB): The Data Mining Component of the TerraSAR-X Payload Ground Segment

In this paper we present the Earth Observation Image Librarian (called EOLib) as a new generation of Image Information Mining Systems. EOLib is operated in the Payload Ground Segment of TerraSAR-X. The main goal of EOLib is to provide semantic annotations of satellite image content and offer to the end user a semantic catalogue via a web user interface. Moreover, EOLib has more functionality such as searches based on image metadata and semantics, visual exploration of the image archives, metadata extraction, etc. The system consists of components such as a query engine, knowledge discovery in databases, visual data mining, epitome generation, and user services. EOLib is able to ingest a TerraSAR-X scene with 8000×8000 pixels in about three minutes. The EOLib workflow starts with the ingestion of a scene, it continues with the semantic annotation of the image content based on machine learning methods, and it ends with publishing the semantic catalogue and enabling the search by metadata and semantic image descriptions.

[1]  Marcel Worring,et al.  Content-Based Image Retrieval at the End of the Early Years , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  Nuno Vasconcelos,et al.  Bridging the Gap: Query by Semantic Example , 2007, IEEE Transactions on Multimedia.

[3]  Albert Bifet,et al.  Mining Big Data in Real Time , 2013, Informatica.

[4]  Shiyong Cui,et al.  Pattern Retrieval in Large Image Databases Using Multiscale Coarse-to-Fine Cascaded Active Learning , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[5]  Shiyong Cui,et al.  Ratio-Detector-Based Feature Extraction for Very High Resolution SAR Image Patch Indexing , 2013, IEEE Geoscience and Remote Sensing Letters.

[6]  Mihai Datcu,et al.  SAR Image Categorization With Log Cumulants of the Fractional Fourier Transform Coefficients , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Marco Pastori,et al.  Information mining in remote sensing image archives: system concepts , 2003, IEEE Trans. Geosci. Remote. Sens..

[8]  Chi-Ren Shyu,et al.  GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[9]  B. S. Manjunath,et al.  Texture Features for Browsing and Retrieval of Image Data , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Daniele Dietrich,et al.  Data Flow and Workflow Organization—The Data Management for the TerraSAR-X Payload Ground Segment , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Christoph Reck,et al.  Architecture Concept for an Information Mining System for Earth Observation Data , 2013 .

[12]  Mihai Datcu,et al.  Earth-Observation Image Retrieval Based on Content, Semantics, and Metadata , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Mihai Datcu,et al.  Application of Visual Data Mining for Earth Observation Use Cases , 2014 .

[14]  Shiyong Cui,et al.  A Taxonomy for High Resolution SAR Images , 2014 .

[15]  Mihai Datcu,et al.  Information Content of Very High Resolution SAR Images: Study of Feature Extraction and Imaging Parameters , 2013, IEEE Transactions on Geoscience and Remote Sensing.