Skip to main content
SpringerLink
Log in

Polarimetric SAR image classification using collaborative representation based nearest subspace

  • Original Paper
  • Published:
Signal, Image and Video Processing Aims and scope Submit manuscript

Abstract

Polarimetric synthetic radar (PolSAR) images contain a huge volume of polarimetric and spatial features, which can be useful for class discrimination. A nonparametric PolSAR classification method called collaborative representation based nearest subspace (CRNS) is proposed in this paper. CRNS simultaneously removes speckle noise and extracts polarimetric-spatial features in two successive stages. At first, it obtains the collaborative representation of the polarimetric cube. Then, it extracts more continuity information from the neighboring pixels through spatial averaging. The extracted polarimetric-spatial feature cube is then classified by using a regularized version of the nearest subspace classifier. The experimental results on two simulated and real PolSAR images show the superior performance of CRNS compared to other state-of-the-art classifiers in both small and large training sets.

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

Access this article

Log in via an institution

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. Liu, G., Li, Y., Jiao, L., Chen, Y., Shang, R.: Multiobjective evolutionary algorithm assisted stacked autoencoder for PolSAR image classification. Swarm. Evolut. Comput. 60(6), 100794 (2021). https://doi.org/10.1016/j.swevo.2020.100794

  2. Fan, C., Hu, Z., Jia, L., et al.: A novel lossless compression encoding framework for SAR remote sensing images. Signal Image Video Process. 15, 441–448 (2021)

    Article  Google Scholar 

  3. Yang, R., Hu, Z., Liu, Y., Xu, Z.: A novel polarimetric SAR classification method integrating pixel-based and patch-based classification. IEEE Geosci. Remote Sens. Lett. 17(3), 431–435 (2020)

    Article  Google Scholar 

  4. Zhang, Z., Wang, H., Xu, F., Jin, Y.: Complex-valued convolutional neural network and its application in polarimetric SAR image classification. IEEE Trans. Geosci. Remote Sens. 55(12), 7177–7188 (2017)

    Article  Google Scholar 

  5. Geng, J., Wang, H., Fan, J., Ma, X.: SAR image classification via deep recurrent encoding neural networks. IEEE Trans. Geosci. Remote Sens. 56(4), 2255–2269 (2018)

    Article  Google Scholar 

  6. Phruksahiran, N.: Potential performance of polarimetric reference function of SAR data processing by coherent target decomposition. Signal Image Video Process. 15, 1021–1029 (2021)

    Article  Google Scholar 

  7. Gokdag, U., Bilgin, G.: SAR image classification post-processing with multiscale complementary Gaussian kernel weighting. Signal Image Video Process. 15, 1425–1433 (2021). https://doi.org/10.1007/s11760-021-01874-w

  8. Youssef, S.M.: ICTEDCT-CBIR: Integrating curvelet transform with enhanced dominant colors extraction and texture analysis for efficient content-based image retrieval. Comput. Electr. Eng. 38(5), 1358–1376 (2012)

    Article  Google Scholar 

  9. Kowkabi, F., Keshavarz, A.: Using spectral Geodesic and spatial Euclidean weights of neighbourhood pixels for hyperspectral Endmember Extraction preprocessing. ISPRS J. Photogramm. Remote Sens. 158, 201–218 (2019)

    Article  Google Scholar 

  10. Cao, J., Wang, S., Wang, R., Zhang, X., Kwong, S.: Content-oriented image quality assessment with multi-label SVM classifier. Signal Process. Image Commun. 78, 388–397 (2019)

    Article  Google Scholar 

  11. Li, W., Tramel, E.W., Prasad, S., Fowler, J.E.: Nearest regularized subspace for hyperspectral classification. IEEE Trans. Geosci. Remote Sens. 52(1), 477–489 (2014)

    Article  Google Scholar 

  12. Li, J., Huang, X., Gamba, P., Bioucas-Dias, J.M., Zhang, L., Benediktsson, J.A., Plaza, A.: Multiple feature learning for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 53(3), 1592–1606 (2015)

    Article  Google Scholar 

  13. Li, J., Marpu, P., Plaza, A., Bioucas-Dias, J., Benediktsson, J.A.: Generalized composite kernel framework for hyperspectral image classification. IEEE Trans. Geosci. Remote Sens. 51(9), 4816–4829 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Xiong, M., Ran, Q., Li, W., Zou, J., Du, Q.: Hyperspectral image classification using weighted joint collaborative representation. IEEE Geosci. Remote Sens. Lett. 12(6), 1209–1213 (2015)

    Article  Google Scholar 

  16. Zhang, R., Zhang, M.: SAR target recognition based on active contour without edges. J. Syst. Eng. Electron. 28(2), 276–281 (2017)

    Article  Google Scholar 

  17. Sukawattanavijit, C., Chen, J., Zhang, H.: GA-SVM algorithm for improving land-cover classification using SAR and optical remote sensing data. IEEE Geosci. Remote Sens. Lett. 14(3), 284–288 (2017)

    Article  Google Scholar 

  18. Guan, D., Xiang, D., Dong, G., Tang, T., Tang, X., Kuang, G.: SAR image classification by exploiting adaptive contextual information and composite kernels. IEEE Geosci. Remote Sens. Lett. 15(7), 1035–1039 (2018)

    Article  Google Scholar 

  19. Cao, X., Ji, Y., Wang, L., Ji, B., Jiao, L., Han, J.: SAR image change detection based on deep denoising and CNN. IET Image Proc. 13, 1509–1515 (2019)

    Article  Google Scholar 

  20. Shen, H., Lin, L., Li, J., Yuan, Q., Zhao, L.: A residual convolutional neural network for polarimetric SAR image super-resolution. ISPRS J. Photogramm. Remote Sens. 161, 90–108 (2020)

    Article  Google Scholar 

  21. Imani, M., Ghassemian, H.: Feature space discriminant analysis for hyperspectral data feature reduction. ISPRS J. Photogramm. Remote Sens. 102, 1–13 (2015)

    Article  Google Scholar 

  22. Divya, S.V., Paul, S., Pati, U.C.: Structure tensor-based SIFT algorithm for SAR image registration. IET Image Proc. 14, 929–938 (2020)

    Article  Google Scholar 

  23. Fu, B., Wang, Y., Campbell, A., Li, Y., Zhang, B., Yin, S., Xing, Z., Jin, X.: Comparison of object-based and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data. Ecol. Ind. 73, 105–117 (2017)

    Article  Google Scholar 

  24. Imani, M., Ghassemian, H.: Weighted Joint collaborative representation based on median-mean line and angular separation. IEEE Trans. Geosci. Remote Sens. 55(10), 5612–5624 (2017)

    Article  Google Scholar 

  25. Wang, H., Shi, J.: SAR image segmentation algorithm based on Contourlet domain AFMRF model. IET Image Proc. 12, 1124–1130 (2018)

    Article  Google Scholar 

  26. Fernando, L., Ruiz, C., Simioni, J.P., Belloli, T.F., Fernandes, P.C.B., Guasselli, L.A.: Object-based classification of vegetation species in a subtropical wetland using Sentinel-1 and Sentinel-2A images. Sci. Remote Sens. 3, 100017 (2021). https://doi.org/10.1016/j.srs.2021.100017

  27. Gomez, L., Alvarez, L., Mazorra, L., Frery, A.C.: Fully PolSAR image classification using machine learning techniques and reaction-diffusion systems. Neurocomputing 255, 52–60 (2017)

    Article  Google Scholar 

  28. Available in https://earth.esa.int/web/polsarpro/data-sources/sample-datasets, Accessed from Nov 2019

  29. Imani, M., Ghassemian, H.: Binary coding based feature extraction in remote sensing high dimensional data. Inf. Sci. 342, 191–208 (2016)

    Article  Google Scholar 

  30. Cohen, J.: A coefficient of agreement from nominal scales. Educ. Psychol. Meas. 20, 37–46 (1960)

    Article  Google Scholar 

  31. Foody, G.M.: Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Photogramm. Eng. Remote Sens. 70, 627–633 (2004)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran

    Maryam Imani

Authors
  1. Maryam Imani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Maryam Imani.

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

Imani, M. Polarimetric SAR image classification using collaborative representation based nearest subspace. SIViP 16, 1577–1585 (2022). https://doi.org/10.1007/s11760-022-02140-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11760-022-02140-3

Keywords

Search

Navigation