Very-High-Resolution SAR Image Ontologies

IEEE Xplore Abstract - Very-High-Resolution SAR Images and Linked Open Data Analytics Based on Ontologies

Espinoza-Molina, D.; Nikolaou, C.; Dumitru, C.O.; Bereta, K.; Koubarakis, M.; Schwarz, G.; Datcu, M., "Very-High-Resolution SAR Images and Linked Open Data Analytics Based on Ontologies," Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of , vol.8, no.4, pp.1696,1708, April 2015
doi: 10.1109/JSTARS.2014.2371138

Abstract: In this paper, we deal with the integration of multiple sources of information such as Earth observation (EO) synthetic aperture radar (SAR) images and their metadata, semantic descriptors of the image content, as well as other publicly available geospatial data sources expressed as linked open data for posing complex queries in order to support geospatial data analytics. Our approach lays the foundations for the development of richer tools and applications that focus on EO image analytics using ontologies and linked open data. We introduce a system architecture where a common satellite image product is transformed from its initial format into to actionable intelligence information, which
includes image descriptors, metadata, image tiles, and semantic labels resulting in an EO-data model. We also create a SAR image ontology based on our EO-data model and a two-level taxonomy  classification scheme of the image content. We demonstrate our approach by linking high-resolution TerraSAR-X images with information from CORINE Land Cover (CLC), Urban Atlas (UA),  GeoNames, and OpenStreetMap (OSM), which are represented in the standard triple model of the resource description frameworks (RDFs).
keywords: {Data models;Feature
extraction;Geospatial analysis;Ontologies;Semantics;Synthetic aperture radar; Vectors; Analytics; Strabon; TerraSAR-X images; linked open data;ontologies;queries;resource description framework (RDFs)},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6981927&isnumber=7112230

Dumitru, C.O.; Cui, S.; Schwarz, G.; Datcu, M., "Information Content of Very-High-Resolution SAR Images: Semantics, Geospatial Context, and Ontologies," Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of , vol.8, no.4, pp.1635,1650, April 2015
doi: 10.1109/JSTARS.2014.2363595

Abstract: Currently, the amount of collected Earth Observation (EO) data is increasing considerably with a rate of several Terabytes of data per day. As a consequence of this increasing data volume, new concepts for exploration and information retrieval are urgently needed. To this end, we propose to explore satellite image data via an image information mining (IIM) approach in which the main steps are feature extraction, classification, semantic annotation, and interactive query processing. This leads to a new process chain and a robust taxonomy for the retrieved categories capitalizing on human interaction and judgment. We concentrated on land cover categories that can be retrieved from high-resolution synthetic aperture radar (SAR) images of the spaceborne TerraSAR-X instrument, where we annotated different urban areas all over the world and defined a taxonomy element for each prevailing surface cover category. The annotation resulted from a test dataset comprising more than 100 scenes covering diverse areas of Africa, Asia, Europe, the Middle East, and North and South America. The scenes were grouped into several collections with similar source areas and each collection was processed separately in order to discern regional characteristics. In the first processing step, each scene was tiled into patches. Then the features were extracted from each patch by a Gabor filter bank and a support vector machine with relevance feedback classifying the feature sets into user-oriented land cover categories. Finally, the categories were semantically annotated using Google Earth for ground truthing. The annotation followed a multilevel approach that allowed the fusion of information being visible on different resolution levels. The novelty of this paper lies in the fact that a semantic annotation was performed with a large number of high-resolution radar images that allowed the definition of more than 850 surface cover categories. This opens the way toward an automated identification - nd classification of urban areas, infrastructure (e.g., airports), geographic objects (e.g., mountains), industrial installations, military compounds, vegetation, and agriculture. Applications that may result from this work can be a semantic catalog of urban images to be used in crisis situations or after a disaster. In addition, the proposed taxonomies can become a basis for building a semantic catalog of satellite images. Finally, we defined four powerful types of high-level queries. Querying on such high levels provides new opportunities for users to search an image database for specific parameters or semantic relationships.
keywords: {Earth;Feature extraction;Remote sensing;Satellites;Semantics;Synthetic aperture radar;Taxonomy;Annotation;TerraSAR-X;classification;feature extraction;high-resolution images;indexing;ontologies;querying;semantic catalogs;synthetic aperture radar (SAR);taxonomies},
URL: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6960829&isnumber=7112230