高分遥感影像不同形状建筑物半自动提取与规则化

doi:10.3969/j.issn.0255-8297.2022.03.002

Abstract

Abstract: The current methods of interactive extraction of buildings from high-resolution remote sensing images mostly require complex user interaction and most of them only support extraction of buildings with right angles. In order to reduce interaction and achieve high-precision extraction of buildings in different shapes, this paper uses region grow, Gaussian mixture models (GMM), CannyLines edge detection and the max-flow/min-cut segmentation method based on multiple star constraints sequentially to obtain building patch, followed by regularization methods to get the building contours which are consistent with the actual building shapes. The average of F₁ is up to 0.9 in extraction experiments, and the experimental results also show the facility and strong robustness of the proposed method.

Key words: high-resolution remote sensing images, semi-automatic, building extraction, regularization

CLC Number:

P237

CUI Weihong, LI Jia, LIU Yu. Semi-automatic Extraction and Regularization of Buildings of Different Shapes from High-Resolution Remote Sensing Images[J]. Journal of Applied Sciences, 2022, 40(3): 372-388.

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Semi-automatic Extraction and Regularization of Buildings of Different Shapes from High-Resolution Remote Sensing Images

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