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Building Extraction from High-Resolution Remotely Sensed Imagery Based on Multi-subgraph Matching

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Abstract

Building extraction is still a difficult issue in the field of remote sensing. In order to extract the buildings with similar structures efficiently, an algorithm based on multi-subgraph matching is proposed using only the panchromatic high-resolution remotely sensed imagery (RSI). Firstly, scale-invariant feature transform feature is detected within both RSI and building template, and the corresponding graphs are constructed. Then, binary matching rules are defined to reconstruct the graphs to reduce the complexity. At last, according to the homogeneity of the building top, disconnected subgraphs are isolated from the reconstructed graphs. To improve the algorithm accuracy, the matched subgraphs are optimized on the basis of the differences in the structure and size. For verifying the validity of the proposed method, nine representatives are chosen from GF-2 images covering Guangzhou, China. Experimental results show that the precision and recall of the proposed method are 97.73% and 87.16%, respectively, and its overall performance F1 is higher than the three other similar methods.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 41701491), Natural Science Foundation of Fujian Province, China (Grant No. 2017J01464), Special Funds of the Central Government Guiding Local Science and Technology Development (Grant No. 2017L3009) and Program for Changjiang Scholars and Innovative Research Team in University (Grant No. IRT_15R10).

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Authors and Affiliations

  1. Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350117, People’s Republic of China

    Wenzao Shi & Jinqing Liu

  2. Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics Technology, Fujian Normal University, Fuzhou, 350007, People’s Republic of China

    Wenzao Shi & Jinqing Liu

  3. Key Lab of Spatial Data Mining and Information Sharing of Ministry of Education, Fuzhou University, Fuzhou, 350002, People’s Republic of China

    Zhengyuan Mao

  4. National Engineering Research Centre of Geospatial Information Technology, Fuzhou University, Fuzhou, 350002, People’s Republic of China

    Zhengyuan Mao

  5. Spatial Information Engineering Research Centre of Fujian Province, Fuzhou University, Fuzhou, 350002, People’s Republic of China

    Zhengyuan Mao

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  1. Wenzao Shi
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  2. Zhengyuan Mao
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  3. Jinqing Liu
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Correspondence to Wenzao Shi or Zhengyuan Mao.

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Shi, W., Mao, Z. & Liu, J. Building Extraction from High-Resolution Remotely Sensed Imagery Based on Multi-subgraph Matching. J Indian Soc Remote Sens 46, 2003–2013 (2018). https://doi.org/10.1007/s12524-018-0868-x

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  • DOI: https://doi.org/10.1007/s12524-018-0868-x

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