предыдущий|следующий

CNN with local binary patterns for hyperspectral images classification

  • role： First author第一作者
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

  • Email：13526635671@163.com
  • Introduction：魏祥坡，1991年生,男,博士研究生, 研究方向为遥感图像处理与分析。E-mail:13526635671@163.com
WEI Xiangpo1,  
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

YU Xuchu1,  
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

ZHANG Pengqiang1,  
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

ZHI Lu1,  
  • Affiliation：

    Information Engineering University, Zhengzhou 450001, China

YANG Fan1

реферат

The classification of hyperspectral image remains a challenging task because of the complexity of spectral and spatial structures, high dimensionality, and strong correlation between adjacent bands. The combination of spatial and spectral information can provide significant advantage in terms of reducing the uncertainty of the samples because the same object has different spectrums and objects with the same spectrum in a hyperspectral image. The Local Binary Pattern (LBP) has also been introduced for spatial-domain feature extraction and classification of hyperspectral images as a simple but powerful texture descriptor. More recently, deep learning has been proven to be a preferable way to extract nonlinear high-level features because of its hierarchical learning framework. The combination of LBP features and the CNNs can lessen the workload of CNNs because of the discrimination capacity of LBP features. In this paper, a novel classification method combining DC–CNN and LBP features, called LBP Dual-Channel CNN (LBP–DC–CNN), is proposed.In LBP–DC–CNN, original hyperspectral data and LBP features are processed in a DC–CNN framework. On the one hand, original hyperspectral data is fed into a 1D–CNN model to extract original spectral features. On the other hand, LBP features are fed into an identical1D–CNN model to extract hierarchical spatial features further. Next, the fully connected layers of the two 1D–CNN models in the DC–CNN framework is concatenated into a fused layer, thus completing the fusion of spectral features and spatial features. Finally, the fused layer is fed into a softmax layer to conduct classification.(1) The OAs of LBP–DC–CNN are better than those of LBP–CNN and DC–CNN, which validate the feature extraction capacity of the CNNs and the advantage of LBP features. LBP–DC–CNN provides better accuracy than that of DC–CNN, which is an advantage of LBP features compared with the spatial features extracted by 2D–CNN model. In addition, the accuracy of LBP–DC–CNN is better than that of LBP–CNN, which validates the reasonability and discriminative power of the dual-channel CNN framework.(2) The OA of LBP–DC–CNN is apparently superior to those of compared methods, which makes DC–CNN and LBP features advantageous. For the Indian Pines data, LBP–DC–CNN (i.e., 98.54 %) yields approximately 2% higher accuracy than the DC–CNN (i.e., 96.68%)and approximately 4% higher accuracy than the LBP–CNN (i.e., 94.74 %). For the University of Pavia data, LBP–DC–CNN (i.e., 99.73 %) yields approximately 1 % higher accuracy than the DC–CNN (i.e., 98.74 %) and approximately 4 % higher accuracy than the LBP-CNN (i.e., 95.92 %). For the Salinas data, LBP–DC–CNN (i.e., 99.56 %) yields approximately 2 % higher accuracy than the DC–CNN (i.e., 97.33 %) and approximately 5 % higher accuracy than the LBP–CNN (i.e., 94.52 %).(3) LBP–DC–CNN can improve the class-specific accuracy of some ground materials, such as Corn-notill and Soybean-mintill in the Indian Pines data, Asphalt and Bricks in the University of Pavia data, and Grapes_untrained and Vinyard_untrained in the Salinas data. LBP features aremore discriminative than spatial features extracted by 2D–CNN.ResultExperiments were conducted on the Indian Pines dataset, Pavia University dataset, and Salinas dataset to verify the performance of LBP–DC–CNN compared with conventional methods. The results are as follows:

ключеви́че слова́

remote sensing;hyperspectral image;classification;CNN;deep learning;LBP

References

  1. 1.
    Bioucas-Dias J M, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N and Chanussot J. 2013. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine, 1(2): 6-36
  2. 2.
    Chen Y S, Jiang H L, Li C Y, Jia X P and Ghamisi P. 2016. Deep feature extraction and classification of hyperspectral images based on convolutional neural networks. IEEE Transactions on Geoscience and Remote Sensing, 54(10): 6232-6251
  3. 3.
    Chen Y S, Lin Z H, Zhao X, Wang G and Gu Y F. 2014. Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2094-2107
  4. 4.
    Chen Y S, Zhao X and Jia X P. 2015. Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 2381-2392
  5. 5.
    Chen Y S, Zhu L, Ghamisi P, Jia X P, Li G Y and Tang L. 2017. Hyperspectral images classification with Gabor filtering and convolutional neural network. IEEE Geoscience and Remote Sensing Letters, 14(12): 2355-2359
  6. 6.
    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(2015): 258619
  7. 7.
    Ji Z and Nie L H. 2016. Texture image classification with noise-tolerant local binary pattern. Journal of Computer Research and Development, 53(5): 1128-1135
  8. 8.
    Jia S, Deng B, Zhu J S, Jia X P and Li Q Q. 2018. Local binary pattern-based hyperspectral image classification with superpixel guidance. IEEE Transactions on Geoscience and Remote Sensing, 56(2): 749-759
  9. 9.
    Kingma D P and Ba J. 2014. Adam: a method for stochastic optimization. arXiv preprint arXiv: 1412.6980
  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.: 1097-1105
  11. 11.
    Li J, Marpu P R, Plaza A, Bioucas-Dias J M and Benediktsson J A. 2013. Generalized composite kernel framework for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 51(9): 4816-4829
  12. 12.
    Li W, Wu G D, Zhang F and Du Q. 2017. Hyperspectral image classification using deep pixel-pair features. IEEE Transactions on Geoscience and Remote Sensing, 55(2): 844-853
  13. 13.
    Moser G and Serpico S B. 2013. Combining support vector machines and Markov random fields in an integrated framework for contextual image classification. IEEE Transactions on Geoscience and Remote Sensing, 51(5): 2734-2752
  14. 14.
    Pesaresi M and Benediktsson J A. 2001. A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 39(2): 309-320 .
  15. 15.
    Plaza A, Plaza J and Martín G. 2009. Incorporation of spatial constraints into spectral mixture analysis of remotely sensed hyperspectral data//Proceedings of 2009 IEEE International Workshop on Machine Learning for Signal Processing. Grenoble: IEEE: 1-6
  16. 16.
    Shu L, McIsaac K and Osinski G R. 2018. Learning spatial-spectral features for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 56(9): 5138-5147
  17. 17.
    Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv: 1409.1556
  18. 18.
    Ye Z and Bai L. 2017. Hyperspectral image classification based on principal component analysis and local binary patterns. Laser and Optoelectronics Progress, 54(11): 111006.1-111006.10
  19. 19.
    Ye Z, Fowler J E and Bai L. 2017. Spatial-spectral hyperspectral classification using local binary patterns and Markov random fields. Journal of Applied Remote Sensing, 11(3): 035002
  20. 20.
    Zhang H K, Li Y, Zhang Y Z and Shen Q. 2017. Spectral-spatial classification of hyperspectral imagery using a dual-channel convolutional neural network. Remote Sensing Letters, 8(5): 438-447
  21. 21.
    Zhang W and Wang W W. 2015. Face recognition based on local binary pattern and deep learning. Journal of Computer Applications, 35(5): 1474-1478
  22. 22.
    Zhi L, Yu X C and Fu Q Y. 2018. Hyperspectral imagery spatial-spectral classification combining local binary patterns. Journal of Geomatics Science and Technology, 35(1): 65-69, 76 .

Читать полностью

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