Built-up area extraction using data field from high-resolution satellite images

Built-up areas are typical man-made structure in urban environment, and timely and accurately acquiring built-up area layers can provide necessary geo-spatial information for planners and policymakers. In this paper, a data field-based method is proposed for the automated detection of built-up areas from high-resolution satellite images. This method views the local corner features of buildings as mass points and models their spatial interaction and distribution by potential function. Due to the salient potential differences between built-up areas and non-built-up areas, the built-up areas are extracted by threshold based segmentation. Two further post-processing techniques, that is, noise removal and hole filling, significantly improve the detection results. The experimental results indicate that the proposed method shows very good performance, and it, with simple parameter settings and without needing sample information, not only can achieve high extraction accuracy but also can effectively keep the shape and topological structure of built-up areas by further post-processing techniques.