Data fusion of high-resolution satellite imagery and GIS data for automatic building extraction
Automatic building extraction in urban areas has become an intensive research as it contributes to many applications. High-resolution satellite (HRS) imagery is an important data source. However, it is a challenge task to extract buildings with only HRS imagery. Additional information and prior knowledge should be incorporated. c A new approach building extraction is proposed in this study. Data sources are QuickBird imagery and GIS data. The GIS data can provide prior knowledge including position and shape information, and the HRS image has rich spectral, texture features. To fuse these two kinds of features, the HRS image is first segmented into image objects. A graph is built according to the connectivity between the adjacent image objects. Second, the position information of GIS data is used to choose a seed region in the image for each GIS building object. Third, the seed region 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 of 0.142 in miss factor and detection percentage of 89.43% (correctness) and the overall quality of 79.35%.