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Many roof materials are considerable sources of pollutants. A reliable classification approach is required to identify these materials. It is beneficial to fuse the information of hyperspectral and laserscanning data. As the resulting feature space is high dimensional an advanced classifier is needed. Support vector machine classifiers based on kernel composition provide a reasonable mean to cope...
Today space-borne high resolution SAR sensors (e.g., TerraSAR-X, TanDEM-X, SAR-Lupe or Cosmo-SkyMed) provide SAR images up to spatial resolutions of 1-3m and even better in spotlight modes. Hence, one major issue of these missions is the development of methods to automatically derive detailed cartographic information from their data. Especially, the analysis of rural and urban areas is on demand in...
An approach for building reconstruction based on fused information extracted from different data sources, namely LIDAR data (light detection and ranging) and aerial imagery, is proposed. The building reconstruction is performed within the scope of a general surface estimation process. This surface estimation aims at generating a DTM including buildings and vegetation removed. The buildings are reconstructed...
In this paper, we present work on automatic road extraction from high-resolution aerial imagery taken over urban areas. In order to deal with the high complexity of this type of scenes, we integrate detailed knowledge about roads and their context using explicitly formulated scale-dependent models. The knowledge about how and when certain parts of the road and context model are optimally exploited...
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