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Session

📌Poster Session and Networking Aperitivo 🍷
Location: Lower Lobby

Presentation 9

An Extension Of The MF3C Decomposition For PolSAR Data

J. David Ballester-Berman¹, Qinghua Xie², Hongtao Shi³

1: Universitat d'Alacant, Spain; 2: China University of Geosciences, Wuhan, P.R. China; 3: China University of Mining and Technology, Xuzhou, P.R. China

In the present paper we propose a two-component polarimetric SAR (PolSAR) decomposition method for estimating the depolarised and polarised components within the target and their corresponding coherency matrices. This idea stems from early works in the radar polarimetry field initiated by Huynen [1] and Cloude [2]. Later, several fundamental works dealt with the nonuniqueness issue of polarimetric decompositions [3,4]. Alternatively, Freeman and Durden moved to a different strategy based on accounting for the scattering physics describing the interaction of radar signals with the target according to some assumptions [5,6]. That work sparked a research line still active today in the field and focused on model-based decomposition techniques, being the final purpose the characterisation of the Earth surface through the retrieval of both quantitave and qualitative information from the decomposition outcomes (see for example [5, 6, 7, 8, 9, 10, 11, 12, 13, 14]). Nevertheless, the interpretation of polarimetric features is still subject to some ambiguity which can negatively affect both the derived land cover classification algorithms and the bio- and geophysical parameter retrieval techniques. Some recent attempts have dealt with this issue and tried to decouple different polarization channels [15] or improving the extraction of the dominant scattering mechanism through an eigendecomposition-based methodology [16]. The present paper is focused on this same issue, but a totally different methodology is employed instead. The method relies on the previous calculation of the backscattering powers given by the model-free three component (MF3C) decomposition [17], being the 3-D Barakat degree of polarisation [18] the key factor for separating polarised and depolarised backscattering components. Here, we propose to estimate the proportion of the polarised and depolarised contributions for all the elements of the observed coherency matrix under the reflection symmetry assumption. Basically, the proposed decomposition can be regarded as an extension of the MF3C method and, consequently, it enables the exploitation of both model-free and model-based approaches for parameter retrieval.

Indoor multi-frequency datasets acquired over three vegetation samples (i.e. cluster of small fir trees, maize, and rice) from the European Microwave Signature Laboratory (EMSL) have been employed for testing the proposed decomposition. Performance analysis has been supported by a quantitative analysis of the decomposed polarised and depolarised components of the elements of the observed coherency matrix and by the eigendecomposition of both resulting coherency matrices. Overall, it has been observed for the datasets employed that decomposition outcomes are consistent with the overall expected behaviour of polarimetric signatures. Limitations related to the decomposition performance specially regarding the T(1,2) element of the coherency matrix and implementation issues derived from the numerical procedure have been also investigated.

References:

[1] J. Huynen, “Phenomenological theory of radar targets,” Ph.D. dissertation, Dept. Elect. Eng., Math. Comput. Sci, Delft Univ. Technol., Delft, The Netherlands, 1970.

[2] S. R. Cloude, “Radar target decomposition theorems,” Electron. Lett., vol. 21, no. 1, pp. 22–24, 1985.

[3] W. A. Holm and R. M. Barnes, “On radar polarization mixed target state,” in Proc. USA Nat. Radar Conf., 1988, pp. 249–254.

[4] S. R. Cloude and E. Pottier, “A review of target decomposition theorems in radar polarimetry,” IEEE Trans. Geosci. Remote Sens., vol. 34, no. 2, pp. 498–518, Mar. 1996.

[5] Freeman, A. and Durden, S. L. A three component scattering model to describe polarimetric SAR data. In SPIE, Radar Polarimetry, volume 1748, pages 213–224, 1992.

[6] Freeman, A. and Durden, S. L. A three-component scattering model for polarimetric SAR data. IEEE Trans. Geosci. Remote Sens., 36(3):963–973, May 1998

[7] Yamaguchi, Y., Moriyama, T., Ishido, M., and Yamada, H. Four-Component scattering model for polarimetric SAR image decomposition. IEEE Trans. Geosci. Remote Sens., 43(8):1699–1706, 2005.

[8] Yamaguchi, Y., Sato, A., Boerner, W. M., Sato, R., and Yamada, H. Four-component scattering power decomposition with rotation of coherency matrix. IEEE Trans. Geosci. Remote Sens., 49(6):2251–2258, 2011.

[9] van Zyl, J.J., Arii, M., and Kim, Y. Model-based decomposition of polarimetric SAR covariance matrices constrained for nonnegative eigenvalues. IEEE Trans. Geosci. Remote Sens., 49(9):3452– 3459, 2011.

[10] Lee, J.-S., Ainsworth, T. L., and Wang, Y. Generalized polarimetric model-based decompositions using incoherent scattering models. IEEE Trans. Geosci. Remote Sens., 52(5):2474–2491, 2014.

[11] Chen, S. W., Wang, X. S., Xiao, S. P., and Sato, M. General polarimetric model-based decomposition for coherency matrix. IEEE Trans. Geosci. Remote Sens., 52(3):1843–1855, 2014.

[12] Jagdhuber, T., Hajnsek, I., and Papathanassiou, K. P. An iterative generalized hybrid decomposition for soil moisture retrieval under vegetation cover using fully polarimetric SAR. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(8):3911–3922, August 2015.

[13] Singh, G., Malik, R., Mohanty, S., Rathore, V. S., Yamada, K., Umemura, M., and Yamaguchi, Y. Seven-component scattering power decomposition of polsar coherency matrix. IEEE Trans. Geosci. Remote Sens., 57(11):8371–8382, Nov 2019.

[14] Ainsworth, T. L., Wang, Y., and Lee, J.-S. Model-based polarimetric SAR decomposition: An L1 regularization approach. IEEE Trans. Geosci. Remote Sens., 60, 2022.

[15] Han, W., Fu, H., Zhu, J., and Li, N. Decoupling between different polarization channels of polsar data. IEEE Geoscience and Remote Sensing Letters, 20, 2023.

[16] Hanis, D., Hadj-Rabah, K., Belhadj-Aissa, A., and Pallotta, L. Dominant scattering mechanism identification from quad-pol-sar data analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 17:14408–14420, 2024.

[17] Dey, S., Ratha, D., and Frery, A. Target characterization and scattering power decomposition for full and compact polarimetric SAR data. IEEE Trans. Geosci. Remote Sens., 59:3981–3998, May 2021.

[18] Barakat, R. N-fold polarization measures and associated thermodynamic entropy of N partially coherent pencils of radiation. Optica Acta: International Journal of Optics, 30(8):1171–1182, 1983.