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Exploring GIS knowledge to improve building extraction and change detection from VHR imagery in urban areas

Existing studies for building extraction in very high resolution (VHR) images consider little prior knowledge, and thus they have limited accuracies and can only apply to a small proportion of buildings. Combining VHR and GIS data to extract buildings is a feasible way to overcome the drawbacks above. However, the inaccurate positions of GIS data and time changes between the two data make it difficult to fuse them. This study aims at presenting the methods to resolve the position and time inconsistencies between the two data for extracting buildings and finding the changes. The methods begin with line extraction from VHR images. For extracting buildings, a two-level graph with four steps was presented to fuse GIS contours and the extracted line segments from VHR images, including choosing initial building edges, clustering the chosen edges, generating the building hypotheses and scoring building hypotheses. The time inconsistencies (i.e. the changes between the buildings in two data sources) were first identified by a scoring function. Then, the detected inconsistent buildings were further verified by the z-significance test from a statistical perspective. Experimental assessments performed on 10 typical urban regions indicated that the proposed methods are highly robust and convincing.