极化干涉SAR植被参数估计方法研究
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摘要
极化干涉SAR综合了干涉SAR和极化SAR的优点,能够反映目标的物理特性和空间分布特性,从而大大拓展了SAR在微波遥感领域的应用。极化干涉SAR技术利用全极化数据,通过相关矩阵特征分解等技术可以明显提高地表参数反演精度,从而为高精度数字高程信息的提取提供了可能。利用极化干涉SAR实现地表植被参数的反演,对森林资源管理和环境监测具有重要价值。
本文主要研究了极化干涉SAR地表植被高度反演方法,同时对极化相干层析(PCT)算法进行了详细的探讨,所做的主要工作和创新如下:
1、分析了极化干涉SAR不同极化状态的散射机制特性及其对植被区域反演的敏感性;同时研究了极化干涉相干系数最优算法,通过实测数据验证了该算法可获得地表目标的散射机制分离。
2、研究了极化干涉SAR散射相位中心的估计。首先,介绍极化干涉SAR相干散射模型,并系统分析了不同参数对相干性的影响;接着,研究了基于ESPRIT算法的散射相位估计方法,可以获得比较精确的植被层和地表层相位估计;最后,研究了利用植被体散射最优相干系数对散射相位进行补偿的方法,大大减小了植被层衰减作用带来的干涉相位误差。
3、提出了三种极化干涉SAR地表植被高度反演方法。首先,通过分析传统的三阶段植被高度反演算法的不足,提出了基于最优体散射相干系数的植被高度反演算法,通过获取最优体散射相干系数从而使植被参数估计更加准确,应用仿真和实测数据比较分析验证该算法的有效性和优势;接着,提出了一种双基线极化干涉SAR反演算法,并通过比较分析说明了双基线算法能够获得更准确的植被高度估计;最后,提出了联合双频的极化干涉SAR植被高度反演算法,通过仿真数据比较分析,证明了该算法能够获得更高的反演精度,并具有较好的稳健性。
4、研究了极化干涉SAR相干层析算法。阐明了极化相干层析算法原理,并介绍了单基线和双基线极化相干层析算法的处理过程和步骤,最后通过仿真数据说明该算法的能够获得植被垂直结构函数,能够更好地反映地表的散射特性。
Polarimetric interferometric SAR integrates all advantages of interferometry SAR and polarization SAR, which reflects the physical characteristics and spatial distribution properties of targets, so it greatly expands the SAR applications in the field of microwave remote sensing. Through multi-polarimetric SAR data and the correlation matrix eigen-decomposition technique, polarization interference technique can significantly improve the accuracy of the surface parameters inversion, so it provides the possibility for the extraction of high-precision digital elevation information. By retrieving the vegetation parameters, Polarimetric interferometric SAR is of great significance to the forest resource management and environmental monitoring.
This paper mainly studies polarimetric interferometric SAR inversion algorithms of ground vegetation height, while detailed discusses polarization coherence tomography (PCT) algorithm. The mainly work and innovation of this paper are as follows:
(1) Analysis the characteristics of the scattering mechanism for different polarimetry and their sensitivities for vegetation regional; Research the optimal polarization interference coherence coefficient algorithm, and the real data demonstrates the algorithm can obtain the separation of scattering mechanisms on surface target.
(2) Research polarimetric interferometric SAR scattering phase center estimation. First of all, introduce polarimetric interferometric SAR coherent scattering model and analysis different parameters on the impact of coherence; Then study the scattering phase estimation based on ESPRIT algorithm, which can obtain accurate phase estimation of vegetation layer and ground surface; Finally, research the interferometric phase compensation method using optimal volume coherent coefficient, which succeeds reducing interferometric phase error which is brought by the vegetation layer attenuation.
(3) Propose three vegetation parameter inversion algorithms of polarimetric interferometric SAR. First, analysis the traditional three-stage method and propose an improved algorithm based on volume coherence coefficient for vegetation height inversion, which can improve the accuracy of vegetation height estimation by getting the accurate estimate of volume coherence coefficient. The simulated data and real data are used to validate the proposed method; Then propose an improved vegetation height inversion algorithm of dual-baseline polarimetric interferometric SAR, which demonstrate the advantages of dual-baseline algorithm with comparison; Finally, a joint vegetation height inversion method is proposed using dual-frequency, and the simulated experiment indicates that this algorithm can improve retrieval accuracy and have good robustness.
(4) Study the polarization coherence tomography algorithm. Clarify the principles of polarization coherence tomography algorithm, and introduce the processes and steps of single-baseline and double-baseline polarization coherence tomography algorithm. Finally, the simulation experiment demonstrates that the algorithm is able to obtain the vertical structure function of vegetation, and obviously reflect the surface scattering characteristics.
本文主要研究了极化干涉SAR地表植被高度反演方法,同时对极化相干层析(PCT)算法进行了详细的探讨,所做的主要工作和创新如下:
1、分析了极化干涉SAR不同极化状态的散射机制特性及其对植被区域反演的敏感性;同时研究了极化干涉相干系数最优算法,通过实测数据验证了该算法可获得地表目标的散射机制分离。
2、研究了极化干涉SAR散射相位中心的估计。首先,介绍极化干涉SAR相干散射模型,并系统分析了不同参数对相干性的影响;接着,研究了基于ESPRIT算法的散射相位估计方法,可以获得比较精确的植被层和地表层相位估计;最后,研究了利用植被体散射最优相干系数对散射相位进行补偿的方法,大大减小了植被层衰减作用带来的干涉相位误差。
3、提出了三种极化干涉SAR地表植被高度反演方法。首先,通过分析传统的三阶段植被高度反演算法的不足,提出了基于最优体散射相干系数的植被高度反演算法,通过获取最优体散射相干系数从而使植被参数估计更加准确,应用仿真和实测数据比较分析验证该算法的有效性和优势;接着,提出了一种双基线极化干涉SAR反演算法,并通过比较分析说明了双基线算法能够获得更准确的植被高度估计;最后,提出了联合双频的极化干涉SAR植被高度反演算法,通过仿真数据比较分析,证明了该算法能够获得更高的反演精度,并具有较好的稳健性。
4、研究了极化干涉SAR相干层析算法。阐明了极化相干层析算法原理,并介绍了单基线和双基线极化相干层析算法的处理过程和步骤,最后通过仿真数据说明该算法的能够获得植被垂直结构函数,能够更好地反映地表的散射特性。
Polarimetric interferometric SAR integrates all advantages of interferometry SAR and polarization SAR, which reflects the physical characteristics and spatial distribution properties of targets, so it greatly expands the SAR applications in the field of microwave remote sensing. Through multi-polarimetric SAR data and the correlation matrix eigen-decomposition technique, polarization interference technique can significantly improve the accuracy of the surface parameters inversion, so it provides the possibility for the extraction of high-precision digital elevation information. By retrieving the vegetation parameters, Polarimetric interferometric SAR is of great significance to the forest resource management and environmental monitoring.
This paper mainly studies polarimetric interferometric SAR inversion algorithms of ground vegetation height, while detailed discusses polarization coherence tomography (PCT) algorithm. The mainly work and innovation of this paper are as follows:
(1) Analysis the characteristics of the scattering mechanism for different polarimetry and their sensitivities for vegetation regional; Research the optimal polarization interference coherence coefficient algorithm, and the real data demonstrates the algorithm can obtain the separation of scattering mechanisms on surface target.
(2) Research polarimetric interferometric SAR scattering phase center estimation. First of all, introduce polarimetric interferometric SAR coherent scattering model and analysis different parameters on the impact of coherence; Then study the scattering phase estimation based on ESPRIT algorithm, which can obtain accurate phase estimation of vegetation layer and ground surface; Finally, research the interferometric phase compensation method using optimal volume coherent coefficient, which succeeds reducing interferometric phase error which is brought by the vegetation layer attenuation.
(3) Propose three vegetation parameter inversion algorithms of polarimetric interferometric SAR. First, analysis the traditional three-stage method and propose an improved algorithm based on volume coherence coefficient for vegetation height inversion, which can improve the accuracy of vegetation height estimation by getting the accurate estimate of volume coherence coefficient. The simulated data and real data are used to validate the proposed method; Then propose an improved vegetation height inversion algorithm of dual-baseline polarimetric interferometric SAR, which demonstrate the advantages of dual-baseline algorithm with comparison; Finally, a joint vegetation height inversion method is proposed using dual-frequency, and the simulated experiment indicates that this algorithm can improve retrieval accuracy and have good robustness.
(4) Study the polarization coherence tomography algorithm. Clarify the principles of polarization coherence tomography algorithm, and introduce the processes and steps of single-baseline and double-baseline polarization coherence tomography algorithm. Finally, the simulation experiment demonstrates that the algorithm is able to obtain the vertical structure function of vegetation, and obviously reflect the surface scattering characteristics.
引文
[1]吴一戎,洪文,王彦平.极化干涉SAR研究现状与启示.电子与信息学报,2007,29(5):1259-1262
[2] S. R. Cloude, K. P. Papathanassiou. Polarimetric SAR Interferometry. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5):1551-1565
[3] K. P. Papathanassiou, A. Reigber, R. Scheiber, R. Horn, A. Moreira, S. R Cloude. Airborne Polarimetric SAR Interferometry. Geoscience and Remote Sensing (IGARSS’98), Vol.4: 1901-1903
[4] R.N.Treuhaft, S.R.Cloude. The Structure of Oriented Vegetation from Polarimetric Interferometry. IEEE Trans. on GRS, 1999, 37(5): 2620-2624
[5] S. R. Cloude, K. P. Papathanssiou, W. M. Boerner. A Fast Method for Vegetation Correction in Topographic Mapping Using Polarimetric Radar Interferometry. Proceedings of 3rd European SAR Conference EUSAR, 2000, Vol.3:261-264
[6] S. R. Cloude, K. P. Papathanassiou, W.M.Boerner. The Remote Sensing of Oriented Volume Scattering Using Polarimetric Radar Interferometry. Proceedings of International Symposium on Antennas and Propagation, 2000, Vol.4:549-552
[7] K. P. Papathanassiou, S. R. Cloude. Single baseline Polarimetric SAR Interferometry. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(11):2352-2363
[8] H. Yamada, Y. Yamaguchi, E. Rodriguez, Y. Kim, W. M. Boerner. Polarimetric SAR Interferometry for forest Canopy Analysis by using the Super-Resolution Method. International Geoscience and Remote Sensing Symposium(IGARSS’01), Vol.3:1101-1103
[9] H.Yamada, Y.Yamaguchi, et al. Polarimetric SAR Interferometry for Forest Analysis Based on the ESPRIT Algorithm. IEICE Trans. Electron, 2001, 84 (12):1917- 1924
[10] S R. Cloude, K. P. Papathanassiou. Three-stage Inversion Process for Polarimetric SAR Interferometry. IEE Proc Radar Sonar Navig, 2003, 150(3):125-134
[11] M.Tabb, T.Flynn, R.Carande. Full Maximum Likelihood Inversion of PolInSAR Scattering Models. International Geoscience and Remote Sensing Symposium(IGARSS’04), Vol.2:1232-1235
[12] S. R. Cloude, Polarization Coherence Tomography, Radio Science, 2006, 34-38
[13] Z. S. Zhou, S. R. Cloude, Application of Polarization Coherence Tomography to GB-POLInSAR Data. International Geoscience and Remote Sensing Symposium(IGARSS’05), Vol.4:4040-4043
[14] J. S. Lee, S. R. Cloude, K. P. Papathanassiou, M. R. Grunes, I. H. Woodhouse. Speckle Filtering and Coherence Estimation of POLInSAR Data for Forest Applications. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10):2254-2263
[15] S. Guillaso, L. F. Famil, A. Reigber, E. Pottier. Building characterization using L-band polarimetric interferometric SAR data. IEEE Transactions on Geoscience and Remote Sensing Letters, 2005, 3(2):347-351
[16] H. Yamada, H. Okada, Y. Yamaguchi. Accuracy Improvement of ESPRIT based Polarimetric SAR Interferometry for Forest Height Estimation. International Geoscience and Remote Sensing Symposium(IGARSS’05), Vol.6:4077-4080
[17] R. Z. Schneider, K. P. Papathanassiou, I. hajnsek, A. Moreira. Polarimetric Interferometry over Urban Areas: Information Extraction using Coherence Scatterers. International Geoscience and Remote Sensing Symposium(IGARSS’05), Vol.2:1089-1092
[18] J. Praks, Florian Kugler, etc. SAR Coherence Tomography for Boreal Forest with Aid of Laser Measurements. International Geoscience and Remote Sensing Symposium(IGARSS’08), Vol.2 :469-472
[19]杨震,杨汝良.极化合成孔径雷达干涉技术.遥感技术与应用,2001,16(3):139-143
[20]杨震.合成孔径雷达干涉与极化干涉技术研究.博士学位论文,2003,67-97
[21]李新武,郭华东,李震,王长林.用SIR-C航天飞机双频极化干涉雷达估计植被高度的方法研究.高技术通讯,2005,15(7):79-84
[22]杨磊,赵拥军,王志刚.基于功率和相位联合估计TLS-ESPRIT算法的极化干涉SAR数据分析.测绘学报,2007,36(2):163-168
[23]杨磊,赵拥军,王志刚.基于酉ESPRIT算法的极化干涉相位估计.测绘科学,2007,32(2):57-59
[24]张腊梅.L波段PoIInSAR图像地表参数反演方法研究.硕士学位论文,2006,25-26
[25]王超,张红,刘智.星载合成孔径雷达干涉测量.科学出版社,2002,57-59
[26]郭华东,李新武.极化干涉雷达遥感机制及应用.遥感学报,2002,6(6):401-405
[27]戴博伟.多极化合成孔径雷达系统与极化信息处理研究.博士学位论文,2002,37-39
[28] E. Colin, C. Titin-Schnaider, W. Tabbara. An interferometric coherence optimization method in radar polarimetry for high-resolution imagery. IEEE Trans Geosci Remote Sensing, 2006,44(1):167-175
[29] J. L. Gomez-Dans, S. Quegan. Constraint coherence optimization in polarimetric interferometry of layered targets. Proc.of POLINSAR 2005, Vol.4:357-360
[30] M. Qong. Coherence optimization using the polarization state conformation PolInSAR. IEEE Geosci Remote Sens Lett, Jul.2005, 2(3):301–305
[31] R. N. Treuhaft, S. N. Madsen, M. Moghaddam, J. J. van Zyl. Vegetation Characteristics and Surface Topography from Interferometric Radar. Radio Science, 1996, 31:14491485
[32] R. N. Treuhaft, S. R. Cloude. The Structure of Oriented Vegetation from Polarimetric Interferometry. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5):2620-2624
[33] Yong Liang Wang, Hui Chen, Ying Peng Ning,et al.Theory and Algorithm of Spatial Spectrum Estimation[M],Tsinghua University Press,2004,186-191
[34] H.Yamada, Y.Yamaguchi, W.M.Boerner. Forest Height Feature Extraction in Polarimetric SAR Interferometry by using Rotational Invariance Property. International Geoscience and Remote Sensing Symposium(IGARSS‘03), Vol.3:1426-1428
[35] S.Guillaso, L.Ferro-Famil, A.Reigber, et al. Polarimetric Interferometric SAR Data Analysis Based on ESPRIT/MUSIC Methods. Proc. of PolInSAR 2003, Vol.5:1-6
[36] A. Freeman, S. L. Durden. A three-component scattering model for polarimetric SAR data[J]. IEEE Trans. on Geoscience and Remote Sensing, 1998, 36(3):963-973.
[37] R.Horn, DLR Airborne SAR Project E-SAR, Geoscience and Remote Sensing Symposium(IGARSS‘96), Vol.3:1624-1628
[38] R.Horn, R.Scheiber, S.Buckreuss, etc. E-SAR generates level-3 SAR products for ProSmart, International Geoscience and Remote Sensing Symposium(IGARSS‘99), Vol.2:1195-1199
[39] M.Tabb, R.Carande. Robust Inversion of Vegetation Structure Parameter from Low Frequency Polarimetric SAR Interferometry. International Geoscience and Remote Sensing Symposium(IGARSS‘01), Vol.7:3188-3190
[40]陈兵,徐绍剑,张平.单基线PolInSAR反演算法研究.电子信息学报,2008,30(7):1744-1746
[41] T. Flynn, M. Tabb, R. Carande. Coherence Region Shape Extraction for Vegetation Parameter Estimation in Polarimetric SAR Interferometry. International Geoscience and Remote Sensing Symposium(IGARSS‘02), Vol.5:2596-2598
[42] S. R. Cloude. Robust Parameter Estimation Using DualBaseline Polarimetric SAR Interferometry, International Geoscience and Remote Sensing Symposium(IGARSS‘02),Vol.2:838-840
[43] S. R. Cloude, M. L. Williams. A Coherent EM Scattering Model for Dual Baseline POLInSAR, International Geoscience and Remote Sensing Symposium(IGARSS‘03), Vol.3:1423-1425
[44] S. R. Cloude, POLInSAR Regularisation using Dual Frequency Interferometry. IEEE Trans. Geosci. Remote Sensing, 2003, 36(5): 1551-1655
[45]李新武,郭华东,李震,王长林.用SIR-C航天飞机双频极化干涉雷达估计植被高度的方法研究.高技术通讯,2005,15(7):79-84
[46] S. R. Cloude, K. P. Papathanassiou. Forest Vertical Structure Estimation using Coherence Tomography. International Geoscience and Remote Sensing Symposium(IGARSS’08), Vol.5:275-278
[47] J.J. Sharma. Irena Hajnsek. K. P. Papathanassiou. Vertical profile reconstruction with Pol-InSAR data of a subpolar glacier. International Geoscience and Remote Sensing Symposium(IGARSS‘07), Vol.5:1147-1150
[48] S. R. Cloude. Dual-Baseline Coherence Tomography. IEEE Transactions on Geoscience and Remote Sensing, 2007, 4(1):127-131
[2] S. R. Cloude, K. P. Papathanassiou. Polarimetric SAR Interferometry. IEEE Transactions on Geoscience and Remote Sensing, 1998, 36(5):1551-1565
[3] K. P. Papathanassiou, A. Reigber, R. Scheiber, R. Horn, A. Moreira, S. R Cloude. Airborne Polarimetric SAR Interferometry. Geoscience and Remote Sensing (IGARSS’98), Vol.4: 1901-1903
[4] R.N.Treuhaft, S.R.Cloude. The Structure of Oriented Vegetation from Polarimetric Interferometry. IEEE Trans. on GRS, 1999, 37(5): 2620-2624
[5] S. R. Cloude, K. P. Papathanssiou, W. M. Boerner. A Fast Method for Vegetation Correction in Topographic Mapping Using Polarimetric Radar Interferometry. Proceedings of 3rd European SAR Conference EUSAR, 2000, Vol.3:261-264
[6] S. R. Cloude, K. P. Papathanassiou, W.M.Boerner. The Remote Sensing of Oriented Volume Scattering Using Polarimetric Radar Interferometry. Proceedings of International Symposium on Antennas and Propagation, 2000, Vol.4:549-552
[7] K. P. Papathanassiou, S. R. Cloude. Single baseline Polarimetric SAR Interferometry. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(11):2352-2363
[8] H. Yamada, Y. Yamaguchi, E. Rodriguez, Y. Kim, W. M. Boerner. Polarimetric SAR Interferometry for forest Canopy Analysis by using the Super-Resolution Method. International Geoscience and Remote Sensing Symposium(IGARSS’01), Vol.3:1101-1103
[9] H.Yamada, Y.Yamaguchi, et al. Polarimetric SAR Interferometry for Forest Analysis Based on the ESPRIT Algorithm. IEICE Trans. Electron, 2001, 84 (12):1917- 1924
[10] S R. Cloude, K. P. Papathanassiou. Three-stage Inversion Process for Polarimetric SAR Interferometry. IEE Proc Radar Sonar Navig, 2003, 150(3):125-134
[11] M.Tabb, T.Flynn, R.Carande. Full Maximum Likelihood Inversion of PolInSAR Scattering Models. International Geoscience and Remote Sensing Symposium(IGARSS’04), Vol.2:1232-1235
[12] S. R. Cloude, Polarization Coherence Tomography, Radio Science, 2006, 34-38
[13] Z. S. Zhou, S. R. Cloude, Application of Polarization Coherence Tomography to GB-POLInSAR Data. International Geoscience and Remote Sensing Symposium(IGARSS’05), Vol.4:4040-4043
[14] J. S. Lee, S. R. Cloude, K. P. Papathanassiou, M. R. Grunes, I. H. Woodhouse. Speckle Filtering and Coherence Estimation of POLInSAR Data for Forest Applications. IEEE Transactions on Geoscience and Remote Sensing, 2003, 41(10):2254-2263
[15] S. Guillaso, L. F. Famil, A. Reigber, E. Pottier. Building characterization using L-band polarimetric interferometric SAR data. IEEE Transactions on Geoscience and Remote Sensing Letters, 2005, 3(2):347-351
[16] H. Yamada, H. Okada, Y. Yamaguchi. Accuracy Improvement of ESPRIT based Polarimetric SAR Interferometry for Forest Height Estimation. International Geoscience and Remote Sensing Symposium(IGARSS’05), Vol.6:4077-4080
[17] R. Z. Schneider, K. P. Papathanassiou, I. hajnsek, A. Moreira. Polarimetric Interferometry over Urban Areas: Information Extraction using Coherence Scatterers. International Geoscience and Remote Sensing Symposium(IGARSS’05), Vol.2:1089-1092
[18] J. Praks, Florian Kugler, etc. SAR Coherence Tomography for Boreal Forest with Aid of Laser Measurements. International Geoscience and Remote Sensing Symposium(IGARSS’08), Vol.2 :469-472
[19]杨震,杨汝良.极化合成孔径雷达干涉技术.遥感技术与应用,2001,16(3):139-143
[20]杨震.合成孔径雷达干涉与极化干涉技术研究.博士学位论文,2003,67-97
[21]李新武,郭华东,李震,王长林.用SIR-C航天飞机双频极化干涉雷达估计植被高度的方法研究.高技术通讯,2005,15(7):79-84
[22]杨磊,赵拥军,王志刚.基于功率和相位联合估计TLS-ESPRIT算法的极化干涉SAR数据分析.测绘学报,2007,36(2):163-168
[23]杨磊,赵拥军,王志刚.基于酉ESPRIT算法的极化干涉相位估计.测绘科学,2007,32(2):57-59
[24]张腊梅.L波段PoIInSAR图像地表参数反演方法研究.硕士学位论文,2006,25-26
[25]王超,张红,刘智.星载合成孔径雷达干涉测量.科学出版社,2002,57-59
[26]郭华东,李新武.极化干涉雷达遥感机制及应用.遥感学报,2002,6(6):401-405
[27]戴博伟.多极化合成孔径雷达系统与极化信息处理研究.博士学位论文,2002,37-39
[28] E. Colin, C. Titin-Schnaider, W. Tabbara. An interferometric coherence optimization method in radar polarimetry for high-resolution imagery. IEEE Trans Geosci Remote Sensing, 2006,44(1):167-175
[29] J. L. Gomez-Dans, S. Quegan. Constraint coherence optimization in polarimetric interferometry of layered targets. Proc.of POLINSAR 2005, Vol.4:357-360
[30] M. Qong. Coherence optimization using the polarization state conformation PolInSAR. IEEE Geosci Remote Sens Lett, Jul.2005, 2(3):301–305
[31] R. N. Treuhaft, S. N. Madsen, M. Moghaddam, J. J. van Zyl. Vegetation Characteristics and Surface Topography from Interferometric Radar. Radio Science, 1996, 31:14491485
[32] R. N. Treuhaft, S. R. Cloude. The Structure of Oriented Vegetation from Polarimetric Interferometry. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5):2620-2624
[33] Yong Liang Wang, Hui Chen, Ying Peng Ning,et al.Theory and Algorithm of Spatial Spectrum Estimation[M],Tsinghua University Press,2004,186-191
[34] H.Yamada, Y.Yamaguchi, W.M.Boerner. Forest Height Feature Extraction in Polarimetric SAR Interferometry by using Rotational Invariance Property. International Geoscience and Remote Sensing Symposium(IGARSS‘03), Vol.3:1426-1428
[35] S.Guillaso, L.Ferro-Famil, A.Reigber, et al. Polarimetric Interferometric SAR Data Analysis Based on ESPRIT/MUSIC Methods. Proc. of PolInSAR 2003, Vol.5:1-6
[36] A. Freeman, S. L. Durden. A three-component scattering model for polarimetric SAR data[J]. IEEE Trans. on Geoscience and Remote Sensing, 1998, 36(3):963-973.
[37] R.Horn, DLR Airborne SAR Project E-SAR, Geoscience and Remote Sensing Symposium(IGARSS‘96), Vol.3:1624-1628
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