A reinforcement of the complete model-based decomposition of polarimetric SAR targets based on canonical Huynen dichotomy

doi:10.7523/j.ucas.2024.046

Abstract

Abstract: As an important technique for the fine interpretation of the polarimetric synthetic aperture radar targets, the model-based polarimetric decomposition is dedicated to additively expanding the complex target scatterings on the canonical models of surface scattering, double-bounce scattering and volume scattering. However, model-based decompositions suffer from negative powers and insufficient utilization of polarimetric information. The complete model-based decomposition (CMD) and its improvement solve these problems, but cause the so-called zero-power degradation further. This is because CMD cannot always extract a surface component and a double-bounce component from the remaining matrix after the removal of the volume scattering, and zero scattering power is then inevitable. CMD is reinforced in this paper. We find that the extraction of the surface and double-bounce components in CMD is essentially a sub-dichotomy of the canonical Huynen dichotomy that prefers the volume scattering. By adaptively upgrading it with the other two sub-dichotomies of the canonical Huynen dichotomy that prefer the surface scattering and dihedral scattering, respectively, the reinforced CMD can always decompose the surface and double-bounce components without degradation, the problem of zero power is thus effectively solved. Qualitative and quantitative comparisons on measured polarimetric SAR dataset are presented to demonstrate the nice performance of the reinforcement.

Key words: polarimetric synthetic aperture radar, polarimetric decomposition, model-based decomposition, scattering modeling, target dichotomy

CLC Number:

TN957.52

LI Jiatong, LI Dong, ZHANG Yunhua, WANG Xun, LU He. A reinforcement of the complete model-based decomposition of polarimetric SAR targets based on canonical Huynen dichotomy[J]. Journal of University of Chinese Academy of Sciences, DOI: 10.7523/j.ucas.2024.046.

References

[1] Cloude S R, Pottier E.A review of target decomposition theorems in radar polarimetry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 498-518. DOI: 10.1109/36.485127.
[2] Lee J S, Pottier E.Polarimetric radar imaging: from basics to applications[J]. Polarimetric Radar Imaging: from Basics to Applications, 2017: 1-398.
[3] Cloude S.Polarisation applications in remote sensing[M]. Oxford: Oxford University Press, 2010.
[4] 王春乐, 禹卫东. Huynen类型目标分解方法的比较与分析[J]. 中国科学院研究生院学报, 2011, 28(3): 402-409. DOI: 10.7523/j.issn.2095-6134.2011.3.019.
[5] Cloude S R, Pottier E.An entropy based classification scheme for land applications of polarimetric SAR[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(1): 68-78. DOI: 10.1109/36.551935.
[6] Yajima Y, Yamaguchi Y, Sato R, et al.POLSAR image analysis of wetlands using a modified four-component scattering power decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008, 46(6): 1667-1673. DOI: 10.1109/TGRS.2008.916326.
[7] Li D, Lu H, Zhang Y H.Solid angle geometry-based modeling of volume scattering with application in the adaptive decomposition of GF-3 data of sea ice in Antarctica[J]. Remote Sensing, 2023, 15(12): 3208. DOI: 10.3390/rs15123208.
[8] Touzi R, Boerner W M, Lee J S, et al.A review of polarimetry in the context of synthetic aperture radar: concepts and information extraction[J]. Canadian Journal of Remote Sensing, 2004, 30(3): 380-407. DOI: 10.5589/m04-013.
[9] Bargiel D.A new method for crop classification combining time series of radar images and crop phenology information[J]. Remote Sensing of Environment, 2017, 198: 369-383. DOI: 10.1016/j.rse.2017.06.022.
[10] Xu F, Jin Y Q.Deorientation theory of polarimetric scattering targets and application to terrain surface classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(10): 2351-2364. DOI: 10.1109/TGRS.2005.855064.
[11] Singh G, Yamaguchi Y, Boerner W M, et al.Monitoring of the March 11, 2011, off-tohoku 9.0 earthquake with super-tsunami disaster by implementing fully polarimetric high-resolution POLSAR techniques[J]. Proceedings of the IEEE, 2013, 101(3): 831-846. DOI: 10.1109/JPROC.2012.2230311.
[12] Yamaguchi Y.Disaster monitoring by fully polarimetric SAR data acquired with ALOS-PALSAR[J]. Proceedings of the IEEE, 2012, 100(10): 2851-2860. DOI: 10.1109/JPROC.2012.2195469.
[13] Dong H W, Zhang L M, Zou B.Exploring vision transformers for polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-15. DOI: 10.1109/TGRS.2021.3137383.
[14] 刘杉, 张风丽, 韦诗莹, 等. 基于极化分解组合的SAR图像视觉优化和建筑物损毁评估[J]. 中国科学院大学学报, 2020, 37(6): 750-759. DOI: 10.7523/j.issn.2095-6134.2020.06.005.
[15] 王懿泽, 孙吉利, 闫成杰, 等. 基于超像素与LightGBM的极化SAR图像地物分类[J]. 中国科学院大学学报, 2023, 40(5): 658-669. DOI: 10.7523/j.ucas.2022.023.
[16] Freeman A, Durden S L.A three-component scattering model for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(3): 963-973. DOI: 10.1109/36.673687.
[17] Yamaguchi Y, Moriyama T, Ishido M, et al.Four-component scattering model for polarimetric SAR image decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2005, 43(8): 1699-1706. DOI: 10.1109/TGRS.2005.852084.
[18] Cui Y, Yamaguchi Y, Yang J, et al.On complete model-based decomposition of polarimetric SAR coherency matrix data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(4): 1991-2001. DOI: 10.1109/TGRS.2013.2257603.
[19] Chen S W, Sato M.General polarimetric model-based decomposition for coherency matrix[C]//2012 IEEE International Geoscience and Remote Sensing Symposium. Munich, Germany. IEEE, 2012: 99-102. DOI: 10.1109/IGARSS.2012.6351627.
[20] An W T, Cui Y, Yang J.Three-component model-based decomposition for polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(6): 2732-2739. DOI: 10.1109/TGRS.2010.2041242.
[21] Sato A, Yamaguchi Y, Singh G, et al.Four-component scattering power decomposition with extended volume scattering model[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(2): 166-170. DOI: 10.1109/LGRS.2011.2162935.
[22] van Zyl J J, Arii M, Kim Y. Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(9): 3452-3459. DOI: 10.1109/TGRS.2011.2128325.
[23] 刘高峰, 李明, 王亚军, 等. 一种新的基于非反射对称非负特征值分解的Freeman分解[J]. 电子与信息学报, 2013, 35(2): 368-375. DOI: 10.3724/SP.J.1146.2012.00897.
[24] Wang C L, Yu W D, Wang R, et al.Comparison of nonnegative eigenvalue decompositions with and without reflection symmetry assumptions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(4): 2278-2287. DOI: 10.1109/TGRS.2013.2259177.
[25] An W T, Xie C H.An improvement on the complete model-based decomposition of polarimetric SAR data[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(11): 1926-1930. DOI: 10.1109/LGRS.2014.2313955.
[26] Li D, Zhang Y H.Unified Huynen phenomenological decomposition of radar targets and its classification applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2016, 54(2): 723-743. DOI: 10.1109/TGRS.2015.2464113.
[27] Touzi R.Target scattering decomposition in terms of roll-invariant target parameters[J]. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(1): 73-84. DOI: 10.1109/TGRS.2006.886176.
[28] Singh G, Yamaguchi Y, Park S-E.General four-component scattering power decomposition with unitary transformation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 3014-3022. DOI: 10.1109/TGRS.2012.2212446.
[29] Yamaguchi Y, Sato A, Boerner W M, et al.Four-component scattering power decomposition with rotation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(6): 2251-2258. DOI: 10.1109/TGRS.2010.2099124.
[30] Li D, Zhang Y H, Liang L T.A mathematical extension to the general four-component scattering power decomposition with unitary transformation of coherency matrix[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(11): 7772-7789. DOI: 10.1109/TGRS.2020.2983758.
[31] Chen S W, Wang X S, Sato M.Uniform polarimetric matrix rotation theory and its applications[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 4756-4770. DOI: 10.1109/TGRS.2013.2284359.
[32] Xiang D L, Tang T, Ban Y F, et al.Unsupervised polarimetric SAR urban area classification based on model-based decomposition with cross scattering[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 116: 86-100. DOI: 10.1016/j.isprsjprs.2016.03.009.
[33] 方保镕, 周继东, 李医民. 矩阵论[M]. 2版. 北京: 清华大学出版社, 2013.
[34] Dey S, Bhattacharya A, Ratha D, et al.Target characterization and scattering power decomposition for full and compact polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2021, 59(5): 3981-3998. DOI:10.1109/TGRS.2020.3010840.
[35] 王贤圆. 极化合成孔径雷达图像特征表示与目标分类方法研究[D]. 成都: 电子科技大学, 2021.
[36] Liu X, Jiao L C, Liu F. PolSF: PolSAR image dataset on San Francisco[EB/OL]. 2019: 1912.07259. (2019-12-16)[2023-12-20]. http://arxiv.org/abs/1912.07259v1.