References

1. 1.
   Aires U R V, Martins V S, Ferreira L B, Huang Y, Heintzman L and Ouyang Y. 2025. Impact of sampling techniques on crop type mapping using multi-temporal composites from Harmonized Landsat-Sentinel images. Computers and Electronics in Agriculture, 237(Part B): 110676
2. 2.
   Cao Z Y. 2022. Polarization information reconstruction based on deep learning. Beijing: Beijing University of Chemical Technology
3. 3.
   Chen J, Liu T Y, Shi Q, Dong J W and Chen Y. 2025. Early-season crop classification: recent developments and prospects. National Remote Sensing Bulletin, 29(6): 1890-1900
4. 4.
   Chen Q H, Liu X G and Chen Q. 2014. An integrated multi-feature segmentation method for fully polarimetric SAR images. Geomatics and Information Science of Wuhan University, 39(12): 1419-1424
5. 5.
   Chen S W, Li Y Z, Wang X S and Xiao S P. 2017. Polarimetric SAR target scattering interpretation in rotation domain: theory and application. Journal of Radars, 6(5): 442-455
6. 6.
   Chen S W, Wang X S, Xiao S P and Sato M. 2014. General polarimetric model-based decomposition for coherency matrix. IEEE Transactions on Geoscience and Remote Sensing, 52(3): 1843-1855
7. 7.
   Cloude S R. 2007. The dual polarization entropy/alpha decomposition: a PALSAR case study. Science and Applications of SAR Polarimetry and Polarimetric Interferometry. Frascati: European Space Agency: 2
8. 8.
   Cloude S R and Pottier E. 1997. An entropy based classification scheme for land applications of polarimetric SAR. IEEE Transactions on Geoscience and Remote Sensing, 35(1): 68-78
9. 9.
   Freeman A. 2007. Fitting a two-component scattering model to polarimetric SAR data from forests. IEEE Transactions on Geoscience and Remote Sensing, 45(8): 2583-2592
10. 10.
    Freeman A and Durden S L. 1998. A three-component scattering model for polarimetric SAR data. IEEE Transactions on Geoscience and Remote Sensing, 36(3): 963-973
11. 11.
    Han W T, Fu H Q, Zhu J J, Zhang S R, Xie Q H and Hu J. 2023. A polarimetric projection-based scattering characteristics extraction tool and its application to PolSAR image classification. ISPRS Journal of Photogrammetry and Remote Sensing, 202: 314-333
12. 12.
    Han W T, Zhou C, Zhu J J, Fu H Q, Xie Q H, Hu J, Wang C C and Gao H. 2025. Research progress and prospects of polarimetric SAR decomposition. Geomatics and Information Science of Wuhan University, 50(8): 1498-1516
13. 13.
    Huynen J R. 1970. Phenomenological theory of radar targets. Delft: Delft University of Technology
14. 14.
    Ji K F and Wu Y R. 2015. Scattering mechanism extraction by a modified Cloude-Pottier decomposition for dual polarization SAR. Remote Sensing, 7(6): 7447-7470
15. 15.
    Krizhevsky A, Sutskever I and Hinton G. 2012. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: ACM
16. 16.
    Lee J S and Pottier E. 2009. Polarimetric radar imaging: from basics to applications. Boca Raton: CRC Press
17. 17.
    Li D, Zhang Y and Liang L. 2020. A mathematical extension to the general four-component scattering power decomposition with unitary transformation of coherency matrix. IEEE Transactions on Geoscience and Remote Sensing, 58(11): 7772-7789
18. 18.
    Li H Z, Wang C, Zhang H and Liu L. 2012. A unified three-component scattering model for polarimetric coherent target decomposition. International Journal of Remote Sensing, 33(9): 2868-2891
19. 19.
    Li Y, Yang J F, Zhang H S, Li G and Chen J. 2024. Review of the studies on remote sensing classification based on polarimetric Synthetic Aperture Radar. National Remote Sensing Bulletin, 28(8): 1835-1853
20. 20.
    Mascolo L, Cloude S R and Lopez-Sanchez J M. 2022. Model-based decomposition of dual-pol SAR data: application to Sentinel-1. IEEE Transactions on Geoscience and Remote Sensing, 60: 5220119
21. 21.
    Shen M H. 2020. Research on coastal zone land cover classification method based on multi-temporal remote sensing. Qingdao: China University of Petroleum (East China)
22. 22.
    Shi L, Sun W D, Yang L, Yang J, Zhao L L, Li P X and Liu Y B. 2025. Evaluation of radiometric and polarimetric errors in the LT-1A satellite data based on tropical forests in the Amazon. Journal of Radars, 14(2): 405-423
23. 23.
    Sun W D, Du X L, Yang J, Shi L, Li M Y, Zhao L L and Hu C. 2025. Reconstruction of full-polarimetric SAR data based on dual-polarimetric observations from the LT-1 Satellite. National Remote Sensing Bulletin, 29(11): 3109-3123
24. 24.
    Wang S L, Qiu Y, Zhao C B, Zou J X and Wang C F. 2025. eDPRF: Efficient differentially private random forest training algorithm. Journal of Software, 36(7): 2929-2946
25. 25.
    Wang T T. 2024. Research on polarimetric SAR target decomposition method based on scattering matrix optimization. Xi'an: Xidian University
26. 26.
    Wu Y H, Ji K F and Yu W X. 2008. Comparison of classification performance of full-, dual- and single-polarization SAR images using SVM. Journal of Remote Sensing, (1): 46-53
27. 27.
    Wu Y R. 2013. The concept of multidimensional synthetic aperture radar imaging. Journal of Radars, 2(2): 135-142
28. 28.
    Xiang D L, Xie Y Z, Cheng J D, Xu Y H, Zhang H and Zheng Y P. 2022. Optical and SAR image registration based on feature decoupling network. IEEE Transactions on Geoscience and Remote Sensing, 60: 1-13
29. 29.
    Xu F, Wang H P and Jin Y Q. 2017. Application of deep learning to SAR target recognition and terrain classification. Journal of Radars, 6(2): 136-148
30. 30.
    Yamaguchi Y, Moriyama T, Ishido M and Yamada H. 2005. Four-component scattering model for polarimetric SAR image decomposition. IEEE Transactions on Geoscience and Remote Sensing, 43(8): 1699–1706
31. 31.
    Yang J, Xiong T and Peng Y N. 2006. Polarimetric SAR image classification by using generalized optimization of polarimetric contrast enhancement. International Journal of Remote Sensing, 27(16): 3413-3424
32. 32.
    Yu F H, Bai J C, Fang J Y, Guo S E, Zhu S F and Xu T Y. 2024. Integration of a parameter combination discriminator improves the accuracy of chlorophyll inversion from spectral imaging of rice. Agriculture Communications, 2(3): 100055
33. 33.
    Zhang B, Perrie W, Li X F and Pichel W G. 2011. Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophysical Research Letters, 38: L10602
34. 34.
    Zhang L M. 2010. Research on feature extraction and detection of man-made target using polarimetric SAR images. Harbin: Harbin Institute of Technology