A Method for Window Extraction with Automatic Sample Selection and Regularity Constraint

GAO Yunlong; ZHANG Fan; QU Xiaozhi; HUANG Xianfeng; CUI Tingting

doi:10.13203/j.whugis20150225

Volume 43 Issue 3

Mar. 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(3): 436-443. > DOI: 10.13203/j.whugis20150225

GAO Yunlong, ZHANG Fan, QU Xiaozhi, HUANG Xianfeng, CUI Tingting. A Method for Window Extraction with Automatic Sample Selection and Regularity Constraint[J]. Geomatics and Information Science of Wuhan University, 2018, 43(3): 436-443. DOI: 10.13203/j.whugis20150225

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A Method for Window Extraction with Automatic Sample Selection and Regularity Constraint

1.
State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
2.
Institute of Remote Sensing Application and Engineering, Chinese People's Public Security University, Beijing 100038, China

Funds:

The National Program on Key Basic Research of China(973 Program) 2012CB719900

the Key Technology Support Program of Hubei Province 220100037

the National Natural Science Foundation of China 41571437

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Author Bio:
GAO Yunlong, PhD candidate, specializes in photogrammetry and remote sensing image processing. E-mail: ylgao@whu.edu.cn
Corresponding author:
ZHANG Fan, PhD, associate professor. E-mail: zhangfan@whu.edu.cn
Received Date: February 18, 2016
Published Date: March 04, 2018

Graphical Abstract

Abstract

Abstract

Windows are important elements of building facade. Therefore, window extraction is of significant value to building structural analysis and facade reconstruction. With respect to the inner structural feature and the distribution regularity among windows, this paper proposed a window extract method based on automatic sample selection and distribution regularity constraint. Firstly, sample selection was performed by a template matching method to select a number of window samples, both the positive and the negative, from one selected window sample. Secondly, JointBoost classifier, trained by the window samples, was employed to achieve preliminary window extraction. Then, windows distribution regularity model, which includes horizontal direction, vertical direction, point of interest and similarity, was defined and reconstructed using the preliminary window extraction. Finally, the final window extraction result was achieved by optimizing preliminary window extraction result on the constraint of distribution regularity model. The experiments proved that the proposed method has high extraction ratio and accurate ratio on images with complicated background, complex window structure and perspective distortion.
- window extraction,
- automatic sample selection,
- regularity constraint,
- machine learning,
- building facade

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References (19)

References

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A Method for Window Extraction with Automatic Sample Selection and Regularity Constraint

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