Précédent|Prochain

Road extraction in rural areas from high resolution remote sensing image using a improved Full Convolution Network

  • role： First author第一作者
  • Affiliation：

    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China

    Hunan Province Key Laboratory of Surveying and mapping Remote Sensing Information Engineering , Hunan University of Science and Technology, Xiangtan 411201, China

  • Email：chkl_hn@163.com
  • Introduction：李朝奎，1967年生，男，教授，研究方向为三维地理建模及其应用。E-mail： chkl_hn@163.com
LI Chaokui12,  
  • Affiliation：

    Remote Sensing Application Promotion Department of Hunan Second Institute of Surveying and Mapping, Changsha 410000, China

ZENG Qiangguo3,  
  • role： Corresponding author通信作者
  • Affiliation：

    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China

    Hunan Province Key Laboratory of Surveying and mapping Remote Sensing Information Engineering , Hunan University of Science and Technology, Xiangtan 411201, China

  • Email：fangjun008@hnust.edu.cn
  • Introduction：方军，1985年生，男，讲师， 研究方向为城市空间分析与建模。E-mail： fangjun008@hnust.edu.cn
FANG Jun12*,  
  • Affiliation：

    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China

    Hunan Province Key Laboratory of Surveying and mapping Remote Sensing Information Engineering , Hunan University of Science and Technology, Xiangtan 411201, China

WU Nei12,  
  • Affiliation：

    National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China

    Hunan Province Key Laboratory of Surveying and mapping Remote Sensing Information Engineering , Hunan University of Science and Technology, Xiangtan 411201, China

WU Kaihua12

résumé

Aiming at the problems of limited research, application of extracting rural roads with high-resolution remote sensing data, and insufficient accuracy of extraction results, a new improved full convolution rural road extraction network model Distributed Convolution network (DC net) is proposed; it combines void convolution and Air Spatial Pyramid Pooling (ASPP) structure. The model extracts the depth feature information of the road based on the full convolution encoding and decoding structures. At the same time, in accordance with the characteristics of the slender rural roads, the ASPP structure based on the hollow convolution is added between the decoded layers to extract the multiscale characteristic information of the road, and the Field of View (FOV) is expanded without sacrificing the spatial resolution of the feature, thereby improving the recognition rate of narrow and fine rural roads. Some suburban areas of Changzhutan city group are considered the experimental objects and the domestic satellite remote sensing image of Gaogaoer as the experimental data. Experimental results are compared with those of the classical methods of all convolution networks. The results show that: (1) the proposed road extraction model DC net is feasible in rural road extraction, with the overall extraction average accuracy reaching 98.72%, indicating high extraction accuracy; (2) comparative results of the effect of several classic full convolution network models on rural road extraction, DC net extraction accuracy and connectivity, as well as tree and shadow shading in the aspect of block are acceptable; (3) the improved road extraction model of the entire proposed convolution network can effectively extract the feature information of rural roads in high-resolution remote sensing images. The overall extraction effect is improved; it provides a new approach for improving the precision of rural road extraction based on domestic high-resolution images.Based on the full convolution network model in deep learning, this paper proposes an improved full convolution rural road extraction model DC net which combines hole convolution and ASPP structure. According to the characteristics of long and thin and connectivity of rural roads, this method combined with hole convolution to expand the receptive field of feature map in the process of model training, which makes the extraction of rural roads more complete.

mots-clés

Full Convolution Network;Dilated Convolution Network;Atrous convolution;ASPP;Rural road extraction

References

  1. 1.
    Chaurasia A and Culurciello E. 2017. LinkNet: exploiting encoder representations for efficient semantic segmentation//2017 IEEE Visual Communications and Image Processing (VCIP). St. Petersburg, FL, USA: IEEE: 1-4
  2. 2.
    Chen L C, Papandreou G, Schroff F and Adam H. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv: 1706.05587
  3. 3.
    Chen L C, Zhu Y K, Papandreou G, Schroff F and Adam H. 2018. Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv preprint arXiv: 1802.02611
  4. 4.
    Cheng G L, Wang Y, Xu S B, Wang H Z, Xiang S M and Pan C H. 2017. Automatic road detection and centerline extraction via cascaded end-to-end convolutional neural network. IEEE Transactions on Geoscience and Remote Sensing, 55(6): 3322-3337
  5. 5.
    Dai J G, Du Y, Fang X X, Wang Y and Miao Z P. 2018. Road extraction method for high resolution optical remote sensing images with multiple feature constraints. Journal of Remote Sensing, 22(5): 777-791
  6. 6.
    Deng J, Dong W, Socher R, Li L J, Li K and Fei-Fei L. 2009. ImageNet: a large-scale hierarchical image database//2009 IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE: 248-255
  7. 7.
    Doshi J. 2018. Residual inception skip network for binary segmentation//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, UT, USA: IEEE: 216-219
  8. 8.
    Glorot X, Bordes A and Bengio Y. 2011. Deep sparse rectifier neural networks//Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics. Fort Lauderdale, FL, USA: PMLR: 315-323
  9. 9.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE: 770-778
  10. 10.
    Heipke C, Mayer H, Wiedemann C and Jamet D. 1997. Evaluation of automatic road extraction. International Archives of Photogrammetry and Remote Sensing, 32:151-161
  11. 11.
    Hu W, Huang Y Y, Wei L, Zhang F and Li H C. 2015. Deep convolutional neural networks for hyperspectral image classification. Journal of Sensors, 2015: 258619
  12. 12.
    Ioffe S and Szegedy C. 2015. Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv: 1502.03167
  13. 13.
    Li C. 2018. Application of remote sensing technology in rural road verification. Geomatics and Spatial Information Technology, 41(5): 48-49, 52
  14. 14.
    Li Y, Guo L L, Rao J, Xu L L and Jin S. 2019. Road segmentation based on hybrid convolutional network for high-resolution visible remote sensing image. IEEE Geoscience and Remote Sensing Letters, 16(4): 613-617
  15. 15.
    Lin X G, Zhang J X, Li H T and Yang Y H. 2009. Semi-automatic extraction of ribbon road from high resolution remotely sensed imagery by a T-shaped template matching. Geomatics and Information Science of Wuhan University, 34(3): 293-296
  16. 16.
    Liu X, Wang G H, Yang H C, Liu Y and Wang Y. 2018. Road extraction from remote sensing image based on fully convolutional networks. Remote Sensing Information, 33(1): 69-75
  17. 17.
    Martin D R, Fowlkes C C and Malik J. 2004. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(5): 530-549
  18. 18.
    Papadomanolaki M, Vakalopoulou M and Karantzalos K. 2017. Patch-based deep learning architectures for sparse annotated very high resolution datasets//2017 Joint Urban Remote Sensing Event. Dubai, United Arab Emirates: IEEE: 1-4
  19. 19.
    Xu Y Y, Xie Z, Feng Y X and Chen Z L. 2018. Road extraction from high-resolution remote sensing imagery using deep learning. Remote Sensing, 10(9): 1461
  20. 20.
    Yi W B, Chen Y H, Tang H and Deng L. 2010. Experimental research on urban road extraction from high-resolution RS images using Probabilistic Topic Models//2010 IEEE International Geoscience and Remote Sensing Symposium. Honolulu, HI, USA: IEEE: 445-448
  21. 21.
    Zeiler M D, Taylor G W and Fergus R. 2015. Adaptive deconvolutional networks for mid and high level feature learning//2011 International Conference on Computer Vision. Barcelona, Spain: IEEE: 2018-2025
  22. 22.
    Zhang R, Zhang J X and Li H T. 2008. Semi-automatic extraction of ribbon roads from high resolution remotely sensed imagery based on angular texture signature and profile match. Journal of Remote Sensing, 12(2): 224-232
  23. 23.
    Zhang Z X, Liu Q J and Wang Y H. 2018. Road extraction by deep residual U-net. IEEE Geoscience and Remote Sensing Letters, 15(5): 749-753
  24. 24.
    Zhong Z L, Li J, Cui W H and Jiang H. 2016. Fully convolutional networks for building and road extraction: preliminary results//2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE: 1591-1594
  25. 25.
    Zhou L C, Zhang C and Wu M. 2018. D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction//Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Salt Lake City, UT, USA: IEEE: 182-186

Lire l'article complet

The above content is generated by Large Model Translation. The translated content is for reference only. We do not assume any commercial or legal responsibilty for any consequences arising from the use of our website