Land use classification of remote sensing images based on convolution neural network

Abstract

In order to further improve the accuracy of land use classification, this paper uses UC Merced land use data set to fine-tune the parameters of CaffeNet, VGG-S, and VGG-F CNN models. Then, the fine-tuned network is used as the feature extractor, and the extracted full connection layer output features are cascaded as the final expression of the image. Finally, the cascaded features are input into the mcODM classifier to obtain the classification results. The results showed that the overall classification accuracy of the multi-structure CNN feature cascade method in UC Merced landuse dataset reached 97.55%, indicating an improvement between 2 and 5% compared with the single CNN model, and the classification accuracy after fine-tuning was improved in the range of 3–5%. In conclusion, this method can effectively improve the expression of features in scene level classification and improve classification performance.

This is a preview of subscription content, access via your institution.

Fig. 1

References

  1. Chen, X. , Yin, X. , Niemier, M. , & Hu, X. S. . (2018). Design and optimization of FeFET-based crossbars for binary convolution neural networks. 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE).

  2. Jun, C. , Yunjun, Z. , Jian, W. , Management, S. O. , Management, S. O. , & University, Y. N. (2019). A comparative study on the development of AI industry between China and USA based on patent analysis. Journal of Intelligence.

  3. Li, W. , Shi, S., Gao, Z., Wei, W., & Gao, S. (2018). Improved deep belief network model and its application in named entity recognition of Chinese electronic medical records. 2018 IEEE 3rd International Conference on Big Data Analysis (ICBDA). IEEE.

  4. Liang, & Xin. (2018). Intelligent detection of structure from remote sensing images based on deep learning method.

  5. Liao, L. , Zhao, Y. , Wei, S. , Wei, Y. , & Wang, J. . (2020). Parameter distribution balanced CNNs. IEEE Transactions on Neural Networks and Learning Systems, PP(99), 1-10.

  6. (2020). Review of land use cover change classification methods based on remote sensing image

  7. Xiao-Jun, J. , Hong-Tao, D. , Zi-Hao, L. , & Li-Hua, Y. E. . (2019). Vein pattern classification based on vggnet convolutional neural network for blue calico. Journal of Optoelectronics·Laser.

  8. Xin, W. , Ke, L. I. , Chen, N. , & Fengchen, H. . (2019). Remote sensing image classification method based on deep convolution neural network and multi-kernel learning. Journal of Electronics & Information Technology.

  9. Xu, K. , Feng, D. , Mi, H. , Zhu, B. , Wang, D. , & Zhang, L. , et al. (2018). Mixup-based acoustic scene classification using multi-channel convolutional neural network. Pacific Rim Conference on Multimedia. Springer, Cham.

  10. Yanfei, P. , Xiaonan, S. , Hong, W. , & Lingling, Z. . (2019). Remote sensing image retrieval combined with deep learning and relevance feedback. Journal of image and graphics.

Download references

Funding

This work is supported by the National Natural Science Foundation of China (No. 61975187) and the Doctoral Scientific Research Foundation of Zhengzhou University of Light Industry (No. 2015BSJJ017).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Xiao Cui.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

This article is part of the Topical Collection on Geological Modeling and Geospatial Data Analysis

Responsible Editor: Keda Cai

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Cui, X., Zheng, Q. et al. Land use classification of remote sensing images based on convolution neural network. Arab J Geosci 14, 267 (2021). https://doi.org/10.1007/s12517-021-06587-5

Download citation

Keywords

  • Convolution neural network
  • Remote sensing image
  • Land use classification
  • Scene classification