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Scene classification of remote sensing image using ensemble convolutional neural network

  • role： First author第一作者
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

  • Email：dong_hang@aliyun.com
  • Introduction：余东行,1993年生,男,硕士研究生,研究方向为摄影测量与遥感、深度学习与遥感影像解译。E-mail: dong_hang@aliyun.com
YU Donghang1,  
  • role： Corresponding author通信作者
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

  • Email：zbm1961@163.com
  • Introduction：张保明,1961年生,男,教授,研究方向为摄影测量与遥感。E-mail: zbm1961@163.com
ZHANG Baoming1*,  
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

ZHAO Chuan1,  
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

GUO Haitao1,  
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

LU Jun1

résumé

Scene classification and recognition of remote sensing image is an important task for image interpretation. High-resolution remote sensing images have rich spatial texture features and semantic information, and their scene categories are diversified. As a result, images in the same category have a huge difference and some images in different categories become similar. which makes images difficult to be classified and recognized correctly. Therefore, choosing effective features and classification algorithms can improve classification performance. In this case, high-precision classification can only be achieved by selecting effective features and classifiers.Traditional scene classification algorithms adopt low-level or mid-level handcrafted features. These features have poor ability to represent high-level semantic information of images, which makes it difficult to achieve satisfactory results on massive complex scene images difficult. Deep learning, especially convolutional neural networks, has made great progress in computer vision. Compared with the methods using handcrafted features, deep learning is currently the most effective way for image classification. The application of a convolutional neural network to remote sensing image classification has achieved higher precision than methods using traditional handcrafted features. However, training a deep convolutional neural network that has too many parameters needs many labeled images, and the process of training is complicated and time-consuming. Generally, a deep convolutional neural network would not perform well with only a few images.A method for image classification using an ensemble convolutional neural network is proposed to improve the performance of convolutional neural networks. The method is composed of three main phases, namely, preprocessing, feature extraction, and ensemble learning. Firstly, the preprocessing stage includes geometry normalization, image intensity normalization, and image augmentation. Secondly, the feature extraction phase considers several deep convolutional neural networks, which have been well pre-trained on ImageNet, and are chosen to remove the last classification layer in the network and to extract different deep features of the same image. Thirdly, a stacking model is constructed in the ensemble learning phase. The stacking model consists of base and meta classifiers. The base classifier is composed of several logistic regression modes that are used to train different features extracted by deep convolutional neural networks. The meta classifier is a support vector machine. Finally, the probability distribution predicted by the base classifier is used to construct a new dataset that would be trained by the meta classifier.Experiments were conducted on two datasets named UCMerced_LandUse and NWPU-RESISC45 to verify the effectiveness of the proposed method. Compared with state-of-the-art methods, the proposed method performed better in overall accuracies. The proposed method could greatly improve performance and achieve overall accuracies of 90.74% and 87.21% on the two datasets, respectively, even with only 10% data used for training.With transfer learning, the features extracted by the deep convolutional neural networks are highly abstract and semantic, which have better ability in classification than other handcrafted features. Through feature fusion and model transferring, the advantages of different features and classification methods could be synthetically utilized. In this way, high classification accuracy could be achieved even with very little training data.

mots-clés

remote sensing image;scene classification;convolutional neural network;transfer learning;ensemble learning

References

  1. 1.
    Chang L, Deng X M, Zhou M Q, Wu Z K, Yuan Y, Yang S and Wang H A. 2016. Convolutional neural networks in image understanding. Acta Automatica Sinica, 42(9): 1300-1312
  2. 2.
    Cheng G, Han J W and Lu X Q. 2017. Remote sensing image scene classification: benchmark and state of the art. Proceedings of the IEEE, 105(10): 1865-1883
  3. 3.
    Chollet F. 2015. Keras[EB/OL][2018-05-01].
  4. 4.
    Chollet F. 2017. Xception: deep learning with depthwise separable convolutions//Proceedings of 2017 IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, HI, USA: IEEE: 1800-1807 [DOI: 10.1109/CVPR.2017.195]
  5. 5.
    Dalal N and Triggs B. 2005. Histograms of oriented gradients for human detection//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego: IEEE: 886-893 [DOI: 10.1109/CVPR.2005.177]
  6. 6.
    He K M, Zhang X Y, Ren S Q, Sun J and Research M. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE: 770-778 [DOI: 10.1109/CVPR.2016.90]
  7. 7.
    He X F, Zou Z R, Tao C and Zhang J X. 2016. Combined saliency with multi-convolutional neural network for high resolution remote sensing scene classification. Acta Geodaetica et Cartographica Sinica, 45(9): 1073-1080
  8. 8.
    Hu F, Xia G S, Hu J W and Zhang L P. 2015. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11): 14680-14707
  9. 9.
    Huang L H, Chen C, Li W and Du Q. 2016. Remote sensing image scene classification using multi-scale completed local binary patterns and fisher vectors. Remote Sensing, 8(6): 483
  10. 10.
    Krizhevsky A, Sutskever I and Hinton G E. 2012. ImageNet classification with deep convolutional neural networks//Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: Curran Associates Inc.: 1907-1105
  11. 11.
    Nogueira K, Penatti O A B and dos Santos J A. 2016. Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recognition, 61:539-556
  12. 12.
    Pan S J and Yang Q. 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22(10): 1345-1359
  13. 13.
    Sheng G F, Yang W, Xu T and Sun H. 2012. High-resolution satellite scene classification using a sparse coding based multiple feature combination. International Journal of Remote Sensing, 33(8): 2395-2412
  14. 14.
    Simonyan K and Zisserman A. 2015. Very Deep convolutional networks for large-scale image recognition//Proceedings of 2015 IEEE International Conference on Learning Representations. San Diego, CA, USA: IEEE:1-14
  15. 15.
    Szegedy C, Vanhoucke V, Ioffe S, Shlens J and Wojna Z.2015. Rethinking the inception architecture for computer vision//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE:2818-2826 [DOI: 10.1109/CVPR.2016.308]
  16. 16.
    Wang J, Luo C, Huang H Q, Zhao H Z and Wang S WQ. 2017. Transferring pre-trained deep cnns for remote scene classification with general features learned from linear PCA network. Remote Sensing, 9(3): 225
  17. 17.
    Weiss K, Khoshgoftaar T M and Wang D D. 2016. A survey of transfer learning. Journal of Big Data, 3: 9
  18. 18.
    Wu H, Liu B Z, Su W C, Zhang W C and Sun J G. 2016. Hierarchical coding vectors for scene level land-use classification. Remote Sensing, 8: 436
  19. 19.
    Xu S H, Mu X D, Zhao P and Ma J. 2016. Scene classification of remote sensing image based on multi-scale feature and deep neural network. Acta Geodaetica et Cartographica Sinica, 45(7): 834-840
  20. 20.
    Yan L, Zhu R X, Liu Y and Mu N. 2017. Scene classification of remote sensing images by optimizing visual vocabulary concerning scene label information. Journal of Remote Sensing, 21(2): 280-290
  21. 21.
    Yang Y and Newsam S. 2010. Bag-of-visual-words and spatial extensions for land-use classification//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. San Jose, California: ACM: 270-279 [DOI: 10.1145/1869790.1869829]
  22. 22.
    Yin J H, Li H and Jia X P. 2015. Crater detection based on GIST features. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(1): 23-29
  23. 23.
    Zhang F, Du B and Zhang L P. 2016. Scene Classification via a gradient boosting random convolutional network framework. IEEE Transactions on Geoscience and Remote Sensing, 54(3): 1793-1802
  24. 24.
    Zheng Z, Fang F, Liu Y Y, Gong X, Guo M Q and Luo Z W. 2018. Joint multi-scale convolution neural network for scene classification of high resolution remote sensing imagery. Acta Geodaetica et Cartographica Sinica, 47(5): 620-630
  25. 25.
    Zhou F Y, Jin L P and Dong J. 2017. Review of convolutional neural network. Chinese Journal of Computers, 40(6): 1229-1251
  26. 26.
    Zhou W X, Newsam S, Li C M and Shao Z F. 2017. Learning low dimensional convolutional neural networks for high-resolution remote sensing image retrieval. Remote Sensing, 9(5): 489
  27. 27.
    Zhou Z H. 2012. Ensemble Methods: Foundations and Algorithms. New York: CRC Press: 83-84
  28. 28.
    Zhou Z H. 2016. Machine Learning. Beijing: Tsinghua University Press .

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