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Journal of the Indian Society of Remote Sensing

, Volume 46, Issue 12, pp 2003–2013 | Cite as

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

  • Wenzao Shi
  • Zhengyuan Mao
  • Jinqing Liu
Research Article
  • 27 Downloads

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.

Keywords

Building extraction SIFT Multi-subgraph matching Remotely sensed imagery Graph segmentation 

Notes

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).

References

  1. Benedek, C., Descombes, X., & Zerubia, J. (2012). Building development monitoring in multitemporal remotely sensed image pairs with stochastic birth-death dynamics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(1), 33–50.CrossRefGoogle Scholar
  2. Blaschke, T., Hay, G. J., Kelly, M., et al. (2014). Geographic object-based image analysis: Towards a new paradigm. ISPRS Journal of Photogrammetry & Remote Sensing, 87(100), 180–191.CrossRefGoogle Scholar
  3. Chaudhuri, D., Kushwaha, N. K., Samal, A., et al. (2015). Automatic building detection from high-resolution satellite images based on morphology and internal gray variance. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 9(5), 1767–1779.CrossRefGoogle Scholar
  4. Chung, K. L., Lin, Y. R., & Huang, Y. H. (2009). Efficient shadow detection of color aerial images based on successive thresholding scheme. IEEE Transactions on Geoscience and Remote Sensing, 47(2), 671–682.CrossRefGoogle Scholar
  5. Ebadi, H., Mokhtarzade, M., & Kabolizade, M. (2014). Automatic building extraction using a fuzzy active contour model. Photogrammetric Engineering & Remote Sensing, 80(11), 1061–1068.CrossRefGoogle Scholar
  6. Hofman, P., Potůčková, M., Hofman, P., et al. (2017). Comprehensive approach for building outline extraction from LiDAR data with accent to a sparse laser scanning point cloud. Geoinformatics FCE CTU, 16(1), 91–102.CrossRefGoogle Scholar
  7. Huang, X., & Zhang, L. (2012). Morphological building/shadow index for building extraction from high-resolution imagery over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 5(1), 161–172.CrossRefGoogle Scholar
  8. Hussain, E., & Shan, J. (2016). Urban building extraction through object-based image classification assisted by digital surface model and zoning map. International Journal of Image & Data Fusion, 7(1), 63–82.CrossRefGoogle Scholar
  9. Lagunas, E., Amin, M. G., Ahmad, F., et al. (2014). Pattern matching for building feature extraction. IEEE Geoscience and Remote Sensing Letters, 11(12), 2193–2197.CrossRefGoogle Scholar
  10. Li, Z., Liu, Z., & Shi, W. (2014). A fast level set algorithm for building roof recognition from high spatial resolution panchromatic images. IEEE Geoscience and Remote Sensing Letters, 11(4), 743–747.CrossRefGoogle Scholar
  11. Liasis, G., & Stavrou, S. (2016). Building extraction in satellite images using active contours and colour features. International Journal of Remote Sensing, 37(5), 1127–1153.CrossRefGoogle Scholar
  12. Lowe, D. G. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2), 91–110.CrossRefGoogle Scholar
  13. Mannokovacs, A., & Sziranyi, T. (2013). Multidirectional building detection in aerial images without shape templates. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W1(1), 227–232.CrossRefGoogle Scholar
  14. Ok, A. O. (2013). Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS Journal of Photogrammetry & Remote Sensing, 86(12), 21–40.CrossRefGoogle Scholar
  15. Ok, A. O., Senaras, C., & Yuksel, B. (2013). Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 51(3), 1701–1717.CrossRefGoogle Scholar
  16. Oztimur Karadag, O., Senaras, C., & Yarman Vural, F. T. (2015). Segmentation fusion for building detection using domain-specific information. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 8(7), 3305–3315.CrossRefGoogle Scholar
  17. Qin, R., Tian, J., & Reinartz, P. (2016). Spatiotemporal inferences for use in building detection using series of very-high-resolution space-borne stereo images. International Journal of Remote Sensing, 37(15), 3455–3476.Google Scholar
  18. Rai, P. K., & Shrivastava, N. (2015). Automatic building extraction based on multiresolution segmentation using remote sensing data. Geographia Polonica, 88(3), 407–421.CrossRefGoogle Scholar
  19. Saito, S., Yamashita, T., & Aoki, Y. (2016). Multiple object extraction from aerial imagery with convolutional neural networks. Journal of Imaging Science and Technology, 60(1), 10402-1–10402-9.CrossRefGoogle Scholar
  20. Senaras, C., & Vural, F. T. Y. (2016). A self-supervised decision fusion framework for building detection. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 9(5), 1780–1791.CrossRefGoogle Scholar
  21. Sirmacek, B., & Unsalan, C. (2009). Urban-area and building detection using SIFT keypoints and graph theory. IEEE Transactions on Geoscience and Remote Sensing, 47(4), 1156–1167.CrossRefGoogle Scholar
  22. Sirmacek, B., & Unsalan, C. (2011). A probabilistic framework to detect buildings in aerial and satellite images. IEEE Transactions on Geoscience and Remote Sensing, 49(1), 211–221.CrossRefGoogle Scholar
  23. Tsai, V. J. D. (2006). A comparative study on shadow compensation of color aerial images in invariant color models. IEEE Transactions on Geoscience and Remote Sensing, 44(6), 1661–1671.CrossRefGoogle Scholar
  24. Zhang, Q., Huang, X., & Zhang, G. (2016). A morphological building detection framework for high-resolution optical imagery over urban areas. IEEE Geoscience and Remote Sensing Letters, 13(9), 1388–1392.CrossRefGoogle Scholar

Copyright information

© Indian Society of Remote Sensing 2018

Authors and Affiliations

  1. 1.Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application, College of Photonic and Electronic EngineeringFujian Normal UniversityFuzhouPeople’s Republic of China
  2. 2.Key Laboratory of OptoElectronic Science and Technology for Medicine of Ministry of Education, Fujian Provincial Key Laboratory of Photonics TechnologyFujian Normal UniversityFuzhouPeople’s Republic of China
  3. 3.Key Lab of Spatial Data Mining and Information Sharing of Ministry of EducationFuzhou UniversityFuzhouPeople’s Republic of China
  4. 4.National Engineering Research Centre of Geospatial Information TechnologyFuzhou UniversityFuzhouPeople’s Republic of China
  5. 5.Spatial Information Engineering Research Centre of Fujian ProvinceFuzhou UniversityFuzhouPeople’s Republic of China

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