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Automatic building extraction in urban areas has become an intensive research as it contributes to manyapplications. High-resolution satellite (HRS) imagery is an important data source. However, it is a challenge task toextract buildings with only HRS imagery. Additional information and prior knowledge should be incorporated.cA new approach building extraction is proposed in this study. Data sources are QuickBird imagery and GISdata. The GIS data can provide prior knowledge including position and shape information, and the HRS image hasrich spectral, texture features. To fuse these two kinds of features, the HRS image is first segmented into imageobjects. A graph is built according to the connectivity between the adjacent image objects. Second, the positioninformation of GIS data is used to choose a seed region in the image for each GIS building object. Third, the seedregion is grown by adding its neighbor regions constrained by the shape of GIS building.  The performance is evaluated according to the manually delineated buildings. The results show performance of0.142 in miss factor and detection percentage of 89.43% (correctness) and the overall quality of 79.35%

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

Data fusion of high-resolution satellite imagery and GIS data for automatic building extraction