Least Squares Matching for Three Dimensional Building Reconstruction

This paper focuses on the problem of semiautomatic extraction of building information from high-resolution satellite images covering urban areas. This information includes buildings height, 2-D structure, and variation detection. An increasing number of applications require accurate and up-to-date cartographic and 3-D data. We introduce a set of accurate and automatic algorithms based on high-resolution remote sensing imagery such as Quickbird. Our method exploits the relationship between buildings height and their shadow in satellite images. Firstly we use our multiple-restriction method to extract the shadow information. Then we can adopt their relationship to compute building height information. In the process of building 2-D information extraction we introduce a new method about morphology used to do edge detection. After that we utilize the methods including image processing, image analyzing, and pattern recognition to detect building 2-D structure. Based on the statistical skewness of image we introduce the conception of variation coefficient. Using this algorithm we can make sure the geographic position of variation detection easily and quickly. Our method involves thresholds, most of them tuned with respect to practical situation and the physical characteristics of the image. Results are shown and discussed on different images.

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Least Squares Matching for Three Dimensional Building Reconstruction

Cited by 4 publications

References 2 publications

Automatic Object Extraction from Aerial Imagery—A Survey Focusing on Buildings

Automatic Object Extraction from Aerial Imagery—A Survey Focusing on Buildings

The Building Area Recognition in Image Based on Faster-RCNN

Semiautomatic extraction of building information and variation detection from high resolution remote sensing images

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