SEMANTIC INDEXING OF TERRASAR-X AND IN SITU DATA FOR URBAN ANALYTICS
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 s...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2015-12-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-1-W5/185/2015/isprsarchives-XL-1-W5-185-2015.pdf |
| Summary: | 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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| ISSN: | 1682-1750 2194-9034 |