Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Comparative evaluation of deep learning-based building extraction techniques using aerial images

항공영상을 이용한 딥러닝 기반 건물객체 추출 기법들의 비교평가

  • Mo, Jun Sang (Dept. of Civil Engineering, Chungbuk National University) ;
  • Seong, Seon Kyeong (Dept. of Civil Engineering, Chungbuk National University) ;
  • Choi, Jae Wan (Dept. of Civil Engineering, Chungbuk National University)
  • Received : 2021.05.18
  • Accepted : 2021.06.23
  • Published : 2021.06.30
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Abstract

Recently, as the spatial resolution of satellite and aerial images has improved, various studies using remotely sensed data with high spatial resolution have been conducted. In particular, since the building extraction is essential for creating digital thematic maps, high accuracy of building extraction result is required. In this manuscript, building extraction models were generated using SegNet, U-Net, FC-DenseNet, and HRNetV2, which are representative semantic segmentation models in deep learning techniques, and then the evaluation of building extraction results was performed. Training dataset for building extraction were generated by using aerial orthophotos including various buildings, and evaluation was conducted in three areas. First, the model performance was evaluated through the region adjacent to the training dataset. In addition, the applicability of the model was evaluated through the region different from the training dataset. As a result, the f1-score of HRNetV2 represented the best values in terms of model performance and applicability. Through this study, the possibility of creating and modifying the building layer in the digital map was confirmed.

최근 위성영상, 항공사진 등의 해상도가 향상됨에 따라 고해상도 원격탐사 자료를 이용한 다양한 연구가 진행되고 있다. 특히, 국토 전역의 건물객체 추출은 수치지도 레이어 및 주제도 작성에 필수적이기 때문에 높은 정확도가 요구된다. 본 연구에서는 딥러닝의 영상처리 기법 중 의미론적 분할에 사용되는 대표적인 모델인 SegNet, U-Net, FC-DenseNet, HRNetV2를 이용하여 건물객체 추출 모델을 생성하고, 이에 따른 모델의 평가를 수행하였다. 학습자료는 다양한 건물들로 이루어진 영상을 이용하여 생성하였고, 평가는 세 지역에 나누어서 진행하였다. 먼저 학습자료와 인접한 지역을 통해 모델의 성능을 평가하였고, 이후 학습자료와 상이한 지역을 통해 모델의 적용성을 평가하였다. 그 결과 HRNetV2 모델이 건물객체 추출의 성능과 적용성 면에서 가장 우수한 결과를 보였다. 본 연구를 통해 수치지도 내 건물레이어 생성 및 수정의 가능성을 확인하였다.

Keywords

Acknowledgement

이 논문은 과학기술정보통신부 및 정보통신산업진흥원의 '2021년 고성능 컴퓨팅 지원' 사업으로부터 지원받아 수행하였으며, 2020년도 정부(교육부)의 제원으로 한국연구재단의 지원을 받아 수행되었음(NRF-2020R1I1A3A04037483).

References

  1. Badrinarayanan, V., Kendall, A., and Cipolla, R. (2017), SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transaction on Pattern Analysis and Machine Intelligence, Vol. 39, No. 12, pp.2481-2495. https://doi.org/10.1109/TPAMI.2016.2644615
  2. Chen, D.Y., Peng, L., Li, W.C., and Wang, Y.D. (2021), Building extraction and number statistics in WUI areas based on UNet structure and ensemble learning, Remote Sensing, Vol. 13, No. 6, pp. 1172. https://doi.org/10.3390/rs13061172
  3. Cheng, Z. and Fu, D. (2020), Remote sensing image segmentation method based on HRNet, IEEE International Geoscience and Remote Sensing Symposium, pp. 6750-6753.
  4. Huang, G., Liu, S., Van der Maaten, L., and Weinberger, K.Q. (2017), Densely connected convolutional networks, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2261-2269.
  5. Jegou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y. (2017), The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation, In proceedings of the IEEE Conference on Computer Vision and Patter Recognition Workshops, pp. 1175-1183.
  6. Joo, G.H., Park, C.H., and Im, H.S. (2020), Performance evaluation of machine learning optimizers, Journal of Korean Electrical and Electronics Engineers, Vol. 24, No. 3, pp. 766-776. (in Korean with English abstract)
  7. Kim, H.J., Kim, J.Y., and Kim, Y.I. (2019), A study on the object-based building extraction using UAV imagery, Journal of Korean Society for Geospatial Information Science, Vol. 27, No. 4, pp. 21-28. (in Korean with English abstract) https://doi.org/10.7319/kogsis.2019.27.4.021
  8. Lee, D.G., Cho, E.J., and Lee, D.C. (2018), Evaluation of building detection from aerial images using region-based convolutional neural network for deep learning, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 36, No. 6, pp. 469-481. (in Korean with English abstract) https://doi.org/10.7848/KSGPC.2018.36.6.469
  9. Pashaei, M., Kamangir, H., Starek, M.J., and Tissot, P. (2020), Review and evaluation of deep learning architectures for efficient land cover mapping with UAS hyper-spatial imagery: a case study over a wetland, Remote Sensing, Vol. 12, No. 6, pp. 959. https://doi.org/10.3390/rs12060959
  10. Ronneberger, O., Fischer, P., and Brox, T. (2015), U-net: Convolutional networksfor biomedical image segmentation, International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer, Cham, pp.234-241.
  11. Seo, H.D. and Kim, E.M. (2020), Object classification and change detection in point clouds using deep learning, Journal of Cadastre & Land InformatiX, Vol. 50, No. 2, pp. 37-51. (in Korean with English abstract) https://doi.org/10.22640/lxsiri.2020.50.2.37
  12. Seo, K.H., Oh, C.W., Kim, D., Lee, M.Y., and Yang, Y.J. (2019), An empirical study on automatic building extraction from aerial images using a deep learning algorithm, Proceedings of Korean Society for Geospatial Information Science, Vol. 2019, No. 5, pp. 243-252. (in Korean with English abstract)
  13. Sun, K., Xiao, B., Liu, D., and Wang, J. (2019), Deep high-resolution representation learning for human pose estimation, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5686-5696.
  14. Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., Liu, D., Mu, Y., Tan, M., Wang, X., Liu, W., and Xiao, B. (2020), Deep high-resolution representation learning for visual recognition, In IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1.
  15. Xu, Y., Wu, L., Xie, Z., and Chen, Z. (2018), Building extraction in very high resolution remote sensing imagery using deep learning and guided filters, Remote Sensing, Vol. 10, No. 1, pp. 144. https://doi.org/10.3390/rs10010144
  16. Zhang, L., Wu, J., Fan, Y., Gao, H., and Chao, Y. (2020), An efficient building extraction method from high spatial resolution remote sensing images based on improved Mask R-CNN, Sensors, Vol. 20, No. 5, pp.1465. https://doi.org/10.3390/s20051465

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