Skip to main content
SpringerLink
Log in

Modified PCA, LDA and LPP feature extraction methods for PolSAR image classification

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

Abstract

Three well-known feature extraction methods are modified for PolSAR image classification in this work. The polarimetric scattering characteristics of the PolSAR image containing randomness degree and scattering mechanism information are utilized to define a scattering coefficient. The defined coefficient is used to modify the principal component analysis (PCA), linear discriminant analysis (LDA) and locality preserving projection (LPP). The simple defined scattering coefficient, without any free parameter or any requirement to training samples, involves the scattering information into the PCA, LDA and LPP transforms. New projection models are developed according to the scattering coefficient. Finally, an edge preserving filter with the first principal component as the guidance image is suggested for importing the spatial characteristics and cleaning the speckle noise. The experimental results show superior performance of the modified feature extraction methods compared to the conventional methods and some state-of-the-art methods.

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
Fig. 5
Fig. 6

Similar content being viewed by others

Data availability

No new data is used in this paper. The datasets used for the experiments are benchmark datasets.

References

  1. Xie W, Jiao L, Zhao J (2016) PolSAR image classification via D-KSVD and NSCT-domain features extraction. IEEE Geosci Remote Sens Lett 13(2):227–231

    Article  Google Scholar 

  2. Ferguson JE, Gunn GE (2022) Polarimetric decomposition of microwave-band freshwater ice SAR data: Review, analysis, and future directions. Remote Sens Environ 280:113176

    Article  Google Scholar 

  3. Abdeen MM, Gaber A, Shokr M, El-Saadawy OA (2018) Minimizing labeling ambiguity during classification process of the geological units covering the central part of the Suez Canal Corridor, Egypt using their radar scattering response. Egypt J Remote Sens Space Sci 21(1):S55–S66

    Google Scholar 

  4. Shi H, Zhao L, Yang J, Lopez-Sanchez JM, Zhao J, Sun W, Shi L, Li P (2021) Soil moisture retrieval over agricultural fields from L-band multi-incidence and multitemporal PolSAR observations using polarimetric decomposition techniques. Remote Sens Environ 261:112485

    Article  Google Scholar 

  5. Horch A, Djemal K, Gafour A, Taleb N (2019) Supervised fusion approach of local features extracted from SAR images for detecting deforestation changes. IET Image Process 13(14):2866–2876

    Article  Google Scholar 

  6. Mohammadian Fini R, Mahlouji M, Shahidinejad A (2022) Performance improvement in face recognition system using optimized Gabor filters. Multimed Tools Appl 81(27):38375–38408

    Article  Google Scholar 

  7. Imani M, Ghassemian H (2019) Morphology-based structure-preserving projection for spectral–spatial feature extraction and classification of hyperspectral data. IET Image Process 13(2):270–279

    Article  Google Scholar 

  8. Samat A, Li E, Du P, Liu S, Miao Z (2021) Improving deep forest via patch-based pooling, morphological profiling, and pseudo labeling for remote sensing image Classification. IEEE J Sel Topics Appl Earth Obs Remote Sens 14:9334–9349

    Article  Google Scholar 

  9. Duan Y, Liu F, Jiao L (2016) Sketching model and higher order neighborhood markov random field-based SAR image segmentation. IEEE Geosci Remote Sens Lett 13(11):1686–1690

    Article  Google Scholar 

  10. Zhang W-C, Wang Y-F, Hu G-H (2008) Compression of multi-polarimetric SAR intensity images based on 3D-matrix transform. IET Image Process 2(4):194–202

    Article  Google Scholar 

  11. Liu F, Shi J, Jiao L, Liu H, Yang S, Wu J, Hao H, Yuan J (2016) Hierarchical semantic model and scattering mechanism based PolSAR image classification. Pattern Recognit 59:325–342

    Article  Google Scholar 

  12. Mullissa AG, Persello C, Reiche J (2021) Despeckling polarimetric SAR data using a multistream complex-valued fully convolutional network. IEEE Geosci Remote Sens Lett, in press

  13. Lin L, Li J, Shen H, Zhao L, Yuan Q, Li X (2022) Low-resolution fully polarimetric SAR and high-resolution single-polarization SAR image fusion network. IEEE Trans Geosci Remote Sens 60:1–17 (Art no. 5216117)

    Google Scholar 

  14. Wu Q, Hou B, Wen Z, Ren Z, Ren B, Jiao L (2020) Structure label matrix completion for PolSAR image classification. Remote Sens 12(3):459

    Article  Google Scholar 

  15. Xiao D, Liu C, Wang Q, Wang C, Zhang X (2020) PolSAR image classification based on dilated convolution and pixel-refining parallel mapping network in the Complex Domain, arXiv:1909.10783v2

  16. Shao Q, Yu L, Guo Y, Xie X, Zou J, Li L (2023) Weakly supervised semantic segmentation of PolSAR image based on improved SEAM. J Phys: Conf Ser 2456:012003

    Google Scholar 

  17. Tian T, Gao L, Song W et al (2018) Feature extraction and classification of VHR images with attribute profiles and convolutional neural networks. Multimed Tools Appl 77:18637–18656

    Article  Google Scholar 

  18. Zhang L, Chen Z, Zou B, Gao Y (2018) Polarimetric SAR Terrain classification using 3D convolutional neural network. Int. Geosci. Remote Sens. Symp. (IGARSS), Valencia, Spain, pp 4551–4554

  19. Liu Q, Lang L (2021) Multi-manifold feature fusion based neural networks for target recognition in complex-valued SAR imagery. ISPRS J Photogramm Remote Sens 180:151–162

    Article  Google Scholar 

  20. Zhang L, Jiao L, Ma W, Duan Y, Zhang D (2019) PolSAR image classification based on multi-scale stacked sparse autoencoder. Neurocomputing 351:167–179

    Article  Google Scholar 

  21. Hua W, Zhang Y, Zhang C, Jin X (2023) PolSAR image classification based on relation network with SWANet. Remote Sens 15(8):2025

    Article  Google Scholar 

  22. Ma Y, Li Y, Zhu L (2019) Land cover classification for polarimetric SAR Image using convolutional neural network and superpixel. Progress Electromagn Res B 83:111–128

    Article  Google Scholar 

  23. Ahishali M, Kiranyaz S, Ince T, Gabbouj M (2021) Classification of polarimetric SAR images using compact convolutional neural networks. GISci Remote Sens 58(1):28–47

    Article  Google Scholar 

  24. Zhang L, Dong H, Zou B (2019) Efficiently utilizing complex-valued PolSAR image data via a multi-task deep learning framework. arXiv:1903.09917v2

  25. Zhu Y, Zhang X, Hu R, Wen G (2018) Adaptive structure learning for low-rank supervised feature selection. Pattern Recognit Lett 109:89–96

    Article  Google Scholar 

  26. Imani M, Ghassemian H (2014) Principal component discriminant analysis for feature extraction and classification of hyperspectral images. 12th Iranian Conference on Intelligent Systems, Bam, Iran, 4–6 Feb

  27. Wang C, Wu P (2021) Image classification based on principal component analysis optimized generative adversarial networks. Multimed Tools Appl 80:9687–9701

    Article  Google Scholar 

  28. Dalmiya CP, Santhi N, Sathyabama B (2019) A novel feature descriptor for automatic change detection in remote sensing images. Egypt J Remote Sens Space Sci 22(2):183–192

    Google Scholar 

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

    Article  Google Scholar 

  30. Liu M, Chen S, Wu J, Lu F, Wang J, Yang T (2018) Configuration recognition via class-dependent structure preserving projections with application to targets in SAR images. IEEE J Sel Topics Appl Earth Obs Remote Sens 11(6):2134–2146

    Article  Google Scholar 

  31. Imani M, Ghassemian H (2015) Feature extraction using weighted training samples. IEEE Geosci Remote Sens Lett 12(7):1387–1391

    Article  Google Scholar 

  32. Liu Y, Jiang B, Feng J et al (2021) Classification of EEG signals for epileptic seizures using feature dimension reduction algorithm based on LPP. Multimed Tools Appl 80:30261–30282

    Article  Google Scholar 

  33. Hu R, Zhu X, Cheng D, He W, Yan Y, Song J, Zhang S (2017) Graph self-representation method for unsupervised feature selection. Neurocomputing 220:130–137

    Article  Google Scholar 

  34. Park S, Moon WM (2007) Unsupervised classification of scattering mechanisms in polarimetric SAR data using fuzzy logic in entropy and alpha plane. IEEE Trans Geosci Remote Sens 45(8):2652–2664

    Article  Google Scholar 

  35. Zhao J, Datcu M, Zhang Z, Xiong H, Yu W (2019) Contrastive-regulated CNN in the complex domain: a method to learn physical scattering signatures from flexible PolSAR images. IEEE Trans Geosci Remote Sens 57(12):10116–10135

    Article  Google Scholar 

  36. Kuo BC, Landgrebe DA (2004) Nonparametric weighted feature extraction for classification. IEEE Trans Geosci Remote Sens 42(5):1096–1105

    Article  Google Scholar 

  37. Gadhiya T, Roy AK (2020) Superpixel-driven optimized wishart network for fast PolSAR image classification using global k -means algorithm. IEEE Trans Geosci Remote Sens 58(1):97–109

    Article  Google Scholar 

  38. Imani M (2021) Patches based edge preserving network for land cover classification using polarimetric synthetic aperture radar images. Int J Remote Sens 42(13):4946–4964

    Article  Google Scholar 

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

    Article  Google Scholar 

  40. Nie X, Ding S, Huang X, Qiao H, Zhang B, Jiang Z (2019) An online multiview learning algorithm for PolSAR data real-time classification. IEEE J Sel Topics Appl Earth Obs Remote Sens 12(1):302–320

    Article  Google Scholar 

  41. Cohen J (1960) A coefficient of agreement from nominal scales. Edu Psychol Meas 20(1):37–46

    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.

Ethics declarations

Conflict of interest

The author declares that she has 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

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Imani, M. Modified PCA, LDA and LPP feature extraction methods for PolSAR image classification. Multimed Tools Appl 83, 41171–41192 (2024). https://doi.org/10.1007/s11042-023-17269-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-023-17269-7

Keywords

Search

Navigation