Earth-Observation Image Retrieval Based on Content, Semantics, and Metadata

Espinoza-Molina, Daniela; Datcu, Mihai

doi:10.1109/tgrs.2013.2262232

Cited by 74 publications

(31 citation statements)

References 27 publications

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“…Here, the SVM uses a large pool of unlabelled data (test data) and only a small set of labelled data (training data) to predict an image semantic class. Starting from a limited number of labeled data, active learning selects the most informative samples to speed up the convergence of accuracy and to reduce the manual effort of labeling (Espinoza-Molina and Datcu, 2013). Active learning has two core components: the sample selection strategy and the model learning, which are repeated until convergence.…”

Section: Semantic Definition and Indexing Of The Image Contentmentioning

confidence: 99%

Semantic Indexing of Terrasar-X and in Situ Data for Urban Analytics

Molina

Alonso

Datcu

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:This paper presents the semantic indexing of TerraSAR-X images and in situ data. Image processing together with machine learning methods, relevance feedback techniques, and human expertise are used to annotate the image content into a land use land cover catalogue. All the generated information is stored into a geo-database supporting the link between different types of information and the computation of queries and analytics. We used 11 TerraSAR-X scenes over Germany and LUCAS as in situ data. The semantic index is composed of about 73 land use land cover categories found in TerraSAR-X test dataset and 84 categories found in LUCAS dataset.

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Section: Semantic Definition and Indexing Of The Image Contentmentioning

confidence: 99%

Semantic Indexing of Terrasar-X and in Situ Data for Urban Analytics

Molina

Alonso

Datcu

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…In case the descriptors are computed locally on patches, which produces multiple descriptors per image, they are first aggregated to produce a singular descriptor entry for each image [11,12]. Finally, the image descriptors are further used in dedicated indexing schemes [9,[13][14][15]] to achieve the end goal of successful retrieval.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Remote Sensing Images with Pattern Spectra Descriptors

Bosilj

Aptoula

Lefèvre

et al. 2016

IJGI

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Abstract:The rapidly increasing volume of visual Earth Observation data calls for effective content based image retrieval solutions, specifically tailored for their high spatial resolution and heterogeneous content. In this paper, we address this issue with a novel local implementation of the well-known morphological descriptors called pattern spectra. They are computationally efficient histogram-like structures describing the global distribution of arbitrarily defined attributes of connected image components. Besides employing pattern spectra for the first time in this context, our main contribution lies in their dense calculation, at a local scale, thus enabling their combination with sophisticated visual vocabulary strategies. The Merced Landuse/Landcover dataset has been used for comparing the proposed strategy against alternative global and local content description methods, where the introduced approach is shown to yield promising performances.

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“…It provides good visualization; however, no real-image processing is achieved. Advanced queries using metadata, semantics, and image content were presented in [15], showing how the integration of multiple sources helps the end-user in finding scenes of interest. Moreover, in recent years, the development of ontologies as explicit formal specifications of the terms in the domain and relations among them [16] has been applied to the EO framework [17].…”

Section: Introductionmentioning

confidence: 99%

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

Espinoza-Molina

Nikolaou

Dumitru

et al. 2015

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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).

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Earth-Observation Image Retrieval Based on Content, Semantics, and Metadata

Cited by 74 publications

References 27 publications

Semantic Indexing of Terrasar-X and in Situ Data for Urban Analytics

Semantic Indexing of Terrasar-X and in Situ Data for Urban Analytics

Retrieval of Remote Sensing Images with Pattern Spectra Descriptors

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

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