Nothing Special   »   [go: up one dir, main page]
Skip to main content

Advertisement

Springer Nature Link
Log in

Remote sensing image classification algorithm based on ridge wave sparse collaborative representation convolutional neural network

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Remote sensing image classification is a crucial link when processing remote sensing images. Through classification, remote sensing images are converted into classified features that can be understood and processed by computers running deep applications. However, the traditional remote sensing image classification methods do not meet the actual application requirements. In recent years, the rapid development of deep learning theory has provided a technical approach for solving remote sensing image classification. However, deep learning has the following problems when applied to remote sensing image classification: First, it is impossible to construct an activation function suitable for deep learning models that matches the characteristics of remote sensing images; second, the deep learning classification models have poor effects. In view of this, this paper first studies the activation function and constructs ridge waves with scale, displacement, and direction information. Because a ridge wave has good compact support characteristics, it can more closely approximate the high-dimensional nonlinear decision function and obtain a more effective activation function, thus solving the activation function problem in deep learning modeling. At the same time, to optimize the classifiers included in deep learning models, this paper proposes a sparse collaborative representation classifier that more fully combines the advantages of sparse representation classifiers and collaborative representation classifiers. It can yield the relationship between the competition and the collaboration of the remote sensing image to be classified and better use the characteristics of the remote sensing image, achieving better classification effect. Based on the above ideas, this paper proposes a remote sensing image classification algorithm based on a ridge wave sparse collaborative representation convolutional neural network. Finally, the method in this paper is verified by experiments on “UC Merced Land Use Dataset” and “RSSCN7 Dataset”. The results show that the average accuracy of the method proposed in this paper is significantly higher than that of machine learning and other deep learning methods, and the method in this paper has better stability and robustness.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Chen Y, Jiao L, Li Y (2017) Multilayer projective dictionary pair learning and sparse autoencoder for PolSAR image classification. IEEE Trans Geosci Remote Sens 55(12):6683–6694

    Article  Google Scholar 

  2. Chen W, Gou S, Wang X (2018) Classification of PolSAR images using multilayer autoencoders and a self-paced learning approach. Remote Sens 10(1):110–119

    Article  Google Scholar 

  3. Chen T, Zhao Y, Guo Y (2019) “Sparsity-regularized feature selection for multi-class remote sensing image classification.” Neural Comput App 1–9

  4. Cheng G, Yang C, Yao X (2018) When deep learning meets metric learning: Remote sensing image scene classification via learning discriminative CNNs. IEEE Trans Geosci Remote Sens 56(5):2811–2821

    Article  Google Scholar 

  5. Fang L, Wang C, Li S (2017) Hyperspectral image classification via multiple-feature-based adaptive sparse representation. IEEE Trans Instrum Meas 66(7):1646–1657

    Article  Google Scholar 

  6. Hamida AB, Benoit A, Lambert P (2018) 3-d deep learning approach for remote sensing image classification. IEEE Trans Geosci Remote Sens 56(8):4420–4434

    Article  Google Scholar 

  7. Haut JM, Paoletti ME, Plaza J (2018) Active learning with convolutional neural networks for hyperspectral image classification using a new bayesian approach. IEEE Trans Geosci Remote Sens 56(11):6440–6461

    Article  Google Scholar 

  8. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  Google Scholar 

  9. Hou B, Ren B, Ju G (2015) SAR image classification via hierarchical sparse representation and multisize patch features. IEEE Geosci Remote Sens Lett 13(1):33–37

    Article  Google Scholar 

  10. http://weegee.vision.ucmerced.edu/datasets/landuse.html

  11. https://hyper.ai/datasets/5440

  12. Jiang J, Chen C, Yu Y (2017) Spatial-aware collaborative representation for hyperspectral remote sensing image classification. IEEE Geosci Remote Sens Lett 14(3):404–408

    Article  Google Scholar 

  13. Li W, Du Q (2014) Joint within-class collaborative representation for hyperspectral image classification. IEEE J Select Topics Appl Earth Observ Remote Sens 7(6):2200–2208

    Article  Google Scholar 

  14. Li W, Du Q, Xiong M (2014) Kernel collaborative representation with Tikhonov regularization for hyperspectral image classification. IEEE Geosci Remote Sens Lett 12(1):48–52

    Article  Google Scholar 

  15. Li Y, Tao C, Tan Y (2016) Unsupervised multilayer feature learning for satellite image scene classification. IEEE Geosci Remote Sens Lett 13(2):157–161

    Article  Google Scholar 

  16. Lillesand T, Kiefer R W, Chipman J (2015) “Remote sensing and image interpretation,” John Wiley & Sons

  17. Lu X, Ma C, Ni B (2018) “Deep regression tracking with shrinkage loss,” Proc Euro Conf Comp Vision (ECCV) 353–369

  18. Lu X, Wang W, Ma C (2019) “See more, know more: Unsupervised video object segmentation with co-attention siamese networks,” Proceedings of the IEEE/CVF Conf Comp Vision Pattern Recogn 3623–3632

  19. Lu X, Wang W, Shen J (2020) “Learning video object segmentation from unlabeled videos,” Proceedings of the IEEE/CVF Conf Comp Vision Pattern Recogn 8960–8970

  20. Maggiori E, Tarabalka Y, Charpiat G (2016) Convolutional neural networks for large-scale remote-sensing image classification. IEEE Trans Geosci Remote Sens 55(2):645–657

    Article  Google Scholar 

  21. Nogueira K, Penatti OAB, dos Santos JA (2017) Towards better exploiting convolutional neural networks for remote sensing scene classification. Pattern Recogn 61:539–556

    Article  Google Scholar 

  22. Qi K, Guan Q, Yang C (2018) Concentric Circle Pooling in Deep Convolutional Networks for Remote Sensing Scene Classification. Remote Sensing 10(6):1–19

    Article  Google Scholar 

  23. Ratha D, Bhattacharya A, Frery AC (2017) Unsupervised classification of PolSAR data using a scattering similarity measure derived from a geodesic distance. IEEE Geosci Remote Sens Lett 15(1):151–155

    Article  Google Scholar 

  24. Rezaee M, Mahdianpari M, Zhang Y (2018) Deep convolutional neural network for complex wetland classification using optical remote sensing imagery. IEEE J Select Topics Appl Earth Observ Remote Sens 11(9):3030–3039

    Article  Google Scholar 

  25. Romero A, Gatta C, Camps-Valls G (2015) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362

    Article  Google Scholar 

  26. Song W, Li M, Zhang P (2017) Unsupervised PolSAR image classification and segmentation using Dirichlet process mixture model and Markov random fields with similarity measure. IEEE J Select Topics Appl Earth Observ Remote Sens 10(8):3556–3568

    Article  Google Scholar 

  27. Wang W, Lu X, Shen J (2019) “Zero-shot video object segmentation via attentive graph neural networks,” Proceedings of the IEEE/CVF Intern Conf Comp Vision 9236–9245

  28. Weng Q, Mao Z, Lin J (2017) “Land-use classification via extreme learning classifier based on deep convolutional features,” IEEE Geosci Remote Sens Lett 14(5):704–8–708

  29. Wu H, Liu B, Su W (2016) Hierarchical coding vectors for scene level land-use classification. Remote Sensing 8(5):1–17

    Google Scholar 

  30. Xia GS, Hu J, Hu F (2017) AID: A benchmark data set for performance evaluation of aerial scene classification. IEEE Trans Geosci Remote Sens 55(7):3965–3981

    Article  Google Scholar 

  31. Xie W, Jiao L, Hou B (2017) POLSAR image classification via Wishart-AE model or Wishart-CAE model. IEEE J Select Topics Appl Earth Observ Remote Sens 10(8):3604–3615

    Article  Google Scholar 

  32. Yusuf A, Alawneh S (2019) A Survey of GPU Implementations for Hyperspectral Image Classification in Remote Sensing. Can J Remote Sens 2019:1–19

    Google Scholar 

  33. Zhang L, Sun L, Zou B (2014) Fully polarimetric SAR image classification via sparse representation and polarimetric features. IEEE J Select Topics Appl Earth Observ Remote Sens 8(8):3923–3932

    Article  Google Scholar 

  34. Zhang C, Pan X, Li H (2018) A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification. ISPRS J Photogramm Remote Sens 140:133–144

    Article  Google Scholar 

  35. Zhao L, Zhang W, Tang P (2019) Analysis of the inter-dataset representation ability of deep features for high spatial resolution remote sensing image scene classification. Multimedia Tools App 78(8):9667–9689

    Article  Google Scholar 

  36. Zhong N, Yang W, Cherian A (2017) Unsupervised classification of polarimetric SAR images via Riemannian sparse coding. IEEE Trans Geosci Remote Sens 55(9):5381–5390

    Article  Google Scholar 

  37. Zou Q, Ni L, Zhang T (2015) Deep learning based feature selection for remote sensing scene classification. IEEE Geosci Remote Sens Lett 12(11):2321–2325

    Article  Google Scholar 

Download references

Acknowledgements

We want to express our sincere gratitude to the anonymous reviews and editors for their efforts in the improvement of the paper.

Funding

This work was supported in part by Natural Science Foundation of Jiangsu Province (No. BK20201479), National Natural Science Foundation of China (No. 61701188), China Postdoctoral Science Foundation (No. 2019M650512), and Natural Science Foundation of Shanxi (No. 201801D221171).

Author information

Authors and Affiliations

  1. School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Fengping An

  2. School of Physics, Electronic Electrical Engineering, Huaiyin Normal University, Huaian, 223300, China

    Fengping An

  3. School of Information, Beijing Wuzi University, Beijing, 100061, China

    Jun-e Liu

Authors
  1. Fengping An
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jun-e Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Fengping An.

Ethics declarations

Conflicts of interest

The authors declare no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

An, F., Liu, Je. Remote sensing image classification algorithm based on ridge wave sparse collaborative representation convolutional neural network. Multimed Tools Appl 80, 33099–33114 (2021). https://doi.org/10.1007/s11042-021-11406-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-021-11406-w

Keywords

Search

Navigation