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Research on High Resolution Remote Sensing Image Classification Based on Convolution Neural Network

  • Wenwen Gong
  • Zhuqing Wang
  • Yong Liang
  • Xin Fan
  • Junmeng Hao
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 545)

Abstract

Traditional classification method based on machine learning algorithm has been widely adopted in very high resolution remote sensing image classification, yet the problem that could not effectively convey a higher level of abstract feature still need to be improved. This paper, relying on the convolution neural network algorithm, has conducted on the high-resolution remote sensing image classification method. Firstly, structure of convolution neural networks was analyzed. The prediction model of convolution neural networks was discussed, and the core of structure was the alternation of the convolution layer and the down sampling layer. Then, the training model of convolution neural networks was researched. By using weights sharing and local connection, convolution neural network, that image could directly entered into, avoids to a certain extent caused by image displacement, dimension change and so on. On this basis, basing on different phase GF-1 remote sensing data and MATLAB development environment under Windows10 operating system, then combining with object-oriented classification technology in image segmentation, this paper built the high resolution remote sensing image classification model based on convolution neural network. Finally, the parameters of the model were tested and analyzed repeatedly, and more accurate model parameters were obtained in this paper. Results show that the mode can effectively improve the classification accuracy, and provide technical support for improving remote sensing image interpretation and formulating sustainable development strategy.

Keywords

High resolution data Convolution neural network Abstract features Image classification 

References

  1. 1.
    Lü, Q., Dou, Y., Niu, X., et al.: Remote sensing image classification based on DBN model. J. Comput. Res. Dev. 51(9), 1911–1918 (2014)Google Scholar
  2. 2.
    Yubao, G., Tianhe, C., Ling, P., et al.: Urban land classification of high resolution remote sensing image using random forests. Bull. Surv. Mapp. 2016(5), 73–76 (2016)Google Scholar
  3. 3.
    Changkun, Y., Chongchang, W., Dingkai, Z., et al.: Classification of high resolution satellite images based on SVM. Mapp. Spat. Geogr. Inf. 2015(9), 142–144 (2015)Google Scholar
  4. 4.
    Haijuan, L., Ting, Z., Hao, S., et al.: Classification and evaluation of high resolution remote sensing images based on RF model. J. Nanjing Univ.: Nat. Sci. Ed. 39(1), 99–103 (2015)Google Scholar
  5. 5.
    Jingguo, L.: Research on remote sensing image classification and modeling based on neural network ensemble. Bull. Surv. Mapp. 2014(3), 17–20 (2014)Google Scholar
  6. 6.
    Dawei, L., Ling, H., Xiaoyong, H.: Classification of high resolution remote sensing images based on depth learning. J. Opt. 36(4), 1–8 (2016)Google Scholar
  7. 7.
    Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106 (1962)CrossRefGoogle Scholar
  8. 8.
    Hinton, G.E., Salakhutdinov, R.R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Simonyan, K., Zisserman, A.: Very deep convolution networks for large-scale image recognition. Comput. Sci. (2014)Google Scholar
  10. 10.
    Mollahosseini, A., Chan, D., Mahoor, M.H., et al.: Going deeper with convolutions, pp. 1–9 (2014)Google Scholar
  11. 11.
    He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. Comput. Sci. (2015)Google Scholar
  12. 12.
    Yongke, Z.: Deep Learning -21 Days, Actual Combat Caffe, vol. 7. Publishing House of Electronics Industry, Beijing (2016)Google Scholar
  13. 13.
    Jianwei, L., Yuan, L., Xionglin, L.: Advances in depth learning. Appl. Res. Comput. 31(7), 1921–1930 (2014)Google Scholar
  14. 14.
    Zhen, W., Maoting, G.: Design and implementation of image recognition algorithm based on convolution neural network. Mod. Comput.: Popul. Ed. 2015(20), 61–66 (2015)Google Scholar
  15. 15.
    Hu, Z.-P., Chen, J.L., Wang, M., et al.: Recent progress on convolutional neural network in pattern recognition. J. Yanshan Univ. (2015)Google Scholar
  16. 16.
    Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: International Conference on Machine Learning, DBLP, pp. 807–814 (2010Google Scholar

Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  • Wenwen Gong
    • 1
  • Zhuqing Wang
    • 1
  • Yong Liang
    • 1
  • Xin Fan
    • 2
  • Junmeng Hao
    • 3
  1. 1.School of Information Science and EngineeringShandong Agricultural UniversityTai’anChina
  2. 2.Tai’an City Intelligence Research Institute of Science and TechnologyTai’anChina
  3. 3.School of Information Science and TechnologyTai Shan UniversityTai’anChina

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