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Classification of high-resolution remote sensing images based on enhanced DeepLab algorithm and adaptive loss function

  • role： First author第一作者
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：xuzeyu@aircas.ac.cn
  • Introduction：许泽宇，1995年生，男，硕士研究生，研究方向为遥感信息计算。E-mail： xuzeyu@aircas.ac.cn
XU Zeyu12,  
  • role： Corresponding author通信作者
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

  • Email：shenzf@radi.ac.cn
  • Introduction：沈占锋，1977年生，男，研究员，研究方向为遥感信息计算。E-mail： shenzf@radi.ac.cn
SHEN Zhanfeng14*,  
  • Affiliation：

    School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China

    State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

LI Yang23,  
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

ZHAO Lifang14,  
  • Affiliation：

    National Engineering Research Center for Geomatics, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China

    College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China

KE Yingming14,  
  • Affiliation：

    National Disaster Reduction Center of China, Beijing 100124, China

LI Lingling5,  
  • Affiliation：

    National Disaster Reduction Center of China, Beijing 100124, China

WEN Qi5

resumen

High-resolution remote sensing images are complex and difficult to classify. Deep learning methods can extract more and deeper information of the features, which is suitable for the classification of high-resolution remote sensing images. This paper studies the high-precision classification of impervious ground, buildings, low vegetation, trees, vehicles, and other features in high-resolution images. An E-DeepLab network model is proposed by combining the characteristics of remote sensing multiground feature classification based on DeepLab v3+ network model. The main improvements are as follows: (1) Improving the combination of encoder and decoder modules and using a simple and effective addition connection method. (2) Reducing the single upsampling multiple, increasing the upsampling layer, and improving the tightness of the connection between the encoder and the decoder. (3) Using the improved adaptive weight loss function to automatically adjust the weight of losses. In accordance with the characteristics of the data, a multichannel training method combining digital surface model and normalized difference vegetation index data is proposed. Using the data from the two regions to conduct experiments, the overall extraction accuracy in Potsdam region reaches 93.2%, the extraction accuracy in buildings reaches 97.8%, the overall extraction accuracy in Vaihingen region reaches 90.7%, and the accuracy of building extraction reaches 96.3%. The accuracy of the two regions is significantly better than the original DeepLab v3+ model and other related models. The visual effect of the classification results is extremely close to the standard map by comparing the classification map and the standard marker map. Results show that the E-DeepLab network has good application value in the feature extraction and classification of high-precision remote sensing images.

palabra clave

remote sensing;high-resolution images;deep learning;E-DeepLab;adaptive weight loss function

References

  1. 1.
    Audebert N, Le Saux B and Lefèvre S. 2018. Beyond RGB: very high resolution urban remote sensing with multimodal deep networks. ISPRS Journal of Photogrammetry and Remote Sensing, 140: 20-32
  2. 2.
    Badrinarayanan V, Kendall A and Cipolla R. 2017. SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(12): 2481-2495
  3. 3.
    Chen K Q, Fu K, Yan M L, Gao X, Sun X and Wei X. 2018a. Semantic segmentation of aerial images with shuffling convolutional neural networks. IEEE Geoscience and Remote Sensing Letters, 15(2): 173-177
  4. 4.
    Chen L C, Papandreou G, Kokkinos I, Murphy K and Yuille A L. 2017a. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40(4): 834-848
  5. 5.
    Chen L C, Papandreou G, Schroff F and Adam H. 2017b. Rethinking atrous convolution for semantic image segmentation. arXiv: 1706.05587
  6. 6.
    Chen L C, Zhu Y K, Papandreou G, Schroff F and Adam H. 2018b. Encoder-decoder with atrous separable convolution for semantic image segmentation//15th European Conference on Computer Vision. Munich, Germany: Springer: 833-851
  7. 7.
    Chollet F. 2017. Xception: deep learning with depthwise separable convolutions//2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE: 1800-1807
  8. 8.
    Gong J Y and Ji S P. 2017. From Photogrammetry to computer vision. Geomatics and Information Science of Wuhan University, 42(11): 1518-1522, 1615
  9. 9.
    Gong J Y and Ji S P. 2018. Photogrammetry and deep learning. Acta Geodaetica et Cartographica Sinica, 47(6): 693-704
  10. 10.
    Guo H D, Chen R S, Xu Z W, Sun J J, Bi J, Wang L Z, Luo J J, Shen H W, Gu D X, Liang D, Shen W Q, Zhang X, Spiess H W and Lengauer T. 2016. Big data in natural sciences, humanities and social sciences-review of the 6th exploratory round table conference. Bulletin of Chinese Academy of Sciences, 31(6): 707-716
  11. 11.
    He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE: 770-778
  12. 12.
    Kussul N, Lavreniuk M, Skakun S and Shelestov A. 2017. Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5): 778-782
  13. 13.
    Li D R, Tong Q X, Li R X, Gong J Y and Zhang L P. 2012. Current issues in high-resolution Earth observation technology. Science China Earth Sciences, 55(7): 1043-1051
  14. 14.
    Li G, Yun I, Kim J and Kin J. 2019. DABNet: depth-wise asymmetric bottleneck for real-time semantic segmentation. arXiv: 1907.11357v2
  15. 15.
    Li Z Q. 2019. Research on Urban Building Extraction from Deep Learning Approach. Beijing: Beijing University of Civil Engineering and Architecture
  16. 16.
    Liu X. 2018. Road Extraction from Remote Sensing Image based on Deep Learning. Xuzhou: China University of Mining and Technology
  17. 17.
    Mnih V. 2013. Machine Learning for Aerial Image Labeling. Canada: University of Toronto
  18. 18.
    Paszke A, Chaurasia A, Kim S and Culurciello E. 2016. ENet: a deep neural network architecture for real-time semantic segmentation. arXiv: 1606.02147v1
  19. 19.
    Peng B. 2018. Research on Road Information Extraction of Remote Sensing Image based on Deep Learning. Chengdu: University of Electronic Science and Technology of China
  20. 20.
    Ronneberger O, Fischer P and Brox T. 2015. U-Net: convolutional networks for biomedical image segmentation//18th International Conference on Medical Image Computing and Computer-Assisted Intervention. Munich, Germany: Springer: 234-241
  21. 21.
    Shelhamer E, Long J and Darrell T. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(4): 640-651
  22. 22.
    Simonyan K and Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. arXiv: 1409.1556
  23. 23.
    Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions//2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE: 1-9
  24. 24.
    Xia M, Cao G, Wang G Y and Shang Y F. 2017. Remote sensing image classification based on deep learning and conditional random fields. Journal of Image and Graphics, 22(9): 1289-1301

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