Multi-level Spatial Semantic Model for Urban House Information Extraction Automatically from Quick Bird Imagery

摘要

Based on the introduction to the characters and constructing flow of space semantic model, the feature space and context of house information in high resolution remote sensing image are analyzed, and the house semantic network model of Quick Bird image is also constructed. Furthermore, the accuracy and practicability of space semantic model are checked up through extracting house information automatically from Quick Bird image after extracting candidate semantic nodes to the image by taking advantage of grey division method, window threshold value method and Hough transformation. Sample result indicates that its type coherence, shape coherence and area coherence are 96.75%, 89.5% and 88% respectively. Thereinto the effect of the extraction of the houses with rectangular roof is the best and that with herringbone and the polygonal roofs is just ideal. However, the effect of the extraction of the houses with round roof is not satisfied and thus they need the further perfection to the semantic model to make them own higher applied value.