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TQR-Net: Tighter Quadrangle-Based Convolutional Neural Network for Dense Building Instance Localization in Remote Sensing Imagery

  • Kaiyu Jiang
  • Qingpeng LiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11903)

Abstract

Building localization in remote sensing imagery (RSI) is widely applied in many geoscience and remote sensing areas. However, many existing methods cannot generate accurate building contours. In this paper, we propose an effective convolutional neural network (CNN) framework, Tighter Quadrangle Network (TQR-Net), to locate buildings with quadrangular contours in RSI. Here, TQR-Net can generate regular contours for each of building targets using a CNN branch which can predict tighter quadrangles in parallel. Then, we train and test TQR-Net on a large building dataset collected from Google Earth, and the experiment results demonstrate that the proposed method can generate high-quality building contours and significantly outperforms other CNN-based detectors.

Keywords

Deep learning Convolutional neural network Building instance localization Remote sensing Tighter quadrangle 

References

  1. 1.
    Kim, T., Muller, J.-P.: Development of a graph-based approach for building detection. Image Vis. Comput. 17(1), 3–14 (1999)CrossRefGoogle Scholar
  2. 2.
    Jung, C.R., Schramm, R.: Rectangle detection based on a windowed Hough transform. In: Proceedings, 17th Brazilian Symposium on Computer Graphics and Image Processing, pp. 113–120 (2004)Google Scholar
  3. 3.
    He, L., et al.: A comparative study of deformable contour methods on medical image segmentation. Image Vis. Comput. 26(2), 141–163 (2008)CrossRefGoogle Scholar
  4. 4.
    Kampffmeyer, M., Salberg, A.-B., Jenssen, R.: Semantic segmentation of small objects and modeling of uncertainty in urban remote sensing images using deep convolutional neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 1–9 (2016)Google Scholar
  5. 5.
    Wu, G., et al.: Automatic building segmentation of aerial imagery using multi-constraint fully convolutional networks. Remote Sens. 10(3), 407 (2018)CrossRefGoogle Scholar
  6. 6.
    Troya-Galvis, A., Gançarski, P., Berti-Équille, L.: Remote sensing image analysis by aggregation of segmentation-classification collaborative agents. Pattern Recogn. 73, 259–274 (2018)CrossRefGoogle Scholar
  7. 7.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  8. 8.
    Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1440–1448 (2015)Google Scholar
  9. 9.
    Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems, pp. 91–99 (2015)Google Scholar
  10. 10.
    He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988 (2017)Google Scholar
  11. 11.
    Ševo, I., Avramović, A.: Convolutional neural network based automatic object detection on aerial images. IEEE Geosci. Remote Sens. Lett. 13(5), 740–744 (2016)CrossRefGoogle Scholar
  12. 12.
    Cheng, G., Zhou, P., Han, J.: Learning rotation-invariant convolutional neural networks for object detection in VHR optical remote sensing images. IEEE Trans. Geosci. Remote Sens. 54(12), 7405–7415 (2016)CrossRefGoogle Scholar
  13. 13.
    Ren, Y., Zhu, C., Xiao, S.: Small object detection in optical remote sensing images via modified Faster R-CNN. Appl. Sci. 8(5), 813 (2018)CrossRefGoogle Scholar
  14. 14.
    Chen, F., et al.: Fast automatic airport detection in remote sensing images using convolutional neural networks. Remote Sens. 10(3), 443 (2018)CrossRefGoogle Scholar
  15. 15.
    Li, K., Cheng, G., Bu, S., You, X.: Rotation-insensitive and context-augmented object detection in remote sensing images. IEEE Trans. Geosci. Remote Sens. 56(4), 2337–2348 (2018)CrossRefGoogle Scholar
  16. 16.
    Li, Q., Mou, L., Jiang, K., Liu, Q., Wang, Y., Zhu, X.X.: Hierarchical region based convolution neural network for multiscale object detection in remote sensing images. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 4355–4358 (2018)Google Scholar
  17. 17.
    Li, Q., Mou, L., Liu, Q., Wang, Y., Zhu, X.X.: HSF-Net: multiscale deep feature embedding for ship detection in optical remote sensing imagery. IEEE Trans. Geosci. Remote Sens. 56(12), 7147–7161 (2018)CrossRefGoogle Scholar
  18. 18.
    Zhang, Q., Wang, Y., Liu, Q., Liu, X., Wang, W.: CNN based suburban building detection using monocular high resolution Google earth images. In: IEEE International Geoscience and Remote Sensing Symposium, pp. 661–664 (2016)Google Scholar
  19. 19.
    Li, Q., Wang, Y., Liu, Q., Wang, W.: Hough transform guided deep feature extraction for dense building detection in remote sensing images. In: International Conference on Acoustics, Speech and Signal Processing, pp. 1872–1876 (2018)Google Scholar
  20. 20.
    Chen, C., Gong, W., Chen, Y., Li, W.: Learning a two-stage CNN model for multi-sized building detection in remote sensing images. Remote Sens. Lett. 10(2), 103–110 (2019)CrossRefGoogle Scholar
  21. 21.
    Pinheiro, P.O., Collobert, R., Dollár, P.: Learning to segment object candidates. In: Advances in Neural Information Processing Systems, pp. 1990–1998 (2015)Google Scholar
  22. 22.
    Dai, J., He, K., Sun, J.: Instance-aware semantic segmentation via multi-task network cascades. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3150–3158 (2016)Google Scholar
  23. 23.
    Li, Y., Qi, H., Dai, J., Ji, X., Wei, Y.: Fully convolutional instance-aware semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2359–2367 (2017)Google Scholar
  24. 24.
    Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5987–5995 (2017)Google Scholar
  25. 25.
    Lin, T.-Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, no. 2, p. 4 (2017)Google Scholar
  26. 26.
    Liu, Y., Jin, L.: Deep matching prior network: toward tighter multi-oriented text detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3454–3461 (2017)Google Scholar
  27. 27.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)Google Scholar
  28. 28.
    Russakovsky, O., et al.: ImageNet large scale visual recognition challenge. Int. J. Comput. Vision 115(3), 211–252 (2015)MathSciNetCrossRefGoogle Scholar
  29. 29.
    He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1026–1034 (2015)Google Scholar
  30. 30.
    Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014).  https://doi.org/10.1007/978-3-319-10602-1_48CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and EngineeringBeihang UniversityBeijingChina

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