Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis
AbstractAccurate building information plays a crucial role for urban planning, human settlements and environmental management. Synthetic aperture radar (SAR) images, which deliver images with metric resolution, allow for analyzing and extracting detailed information on urban areas. In this paper, we consider the problem of extracting individual buildings from SAR images based on domain ontology. By analyzing a building scattering model with different orientations and structures, the building ontology model is set up to express multiple characteristics of individual buildings. Under this semantic expression framework, an object-based SAR image segmentation method is adopted to provide homogeneous image objects, and three categories of image object features are extracted. Semantic rules are implemented by organizing image object features, and the individual building objects expression based on an ontological semantic description is formed. Finally, the building primitives are used to detect buildings among the available image objects. Experiments on TerraSAR-X images of Foshan city, China, with a spatial resolution of 1.25 m × 1.25 m, have shown the total extraction rates are above 84%. The results indicate the ontological semantic method can exactly extract flat-roof and gable-roof buildings larger than 250 pixels with different orientations. View Full-Text
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Gui, R.; Xu, X.; Dong, H.; Song, C.; Pu, F. Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis. Remote Sens. 2016, 8, 708.
Gui R, Xu X, Dong H, Song C, Pu F. Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis. Remote Sensing. 2016; 8(9):708.Chicago/Turabian Style
Gui, Rong; Xu, Xin; Dong, Hao; Song, Chao; Pu, Fangling. 2016. "Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis." Remote Sens. 8, no. 9: 708.
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