基于一般散射模型的Hybrid Freeman/Eigenvalue分解算法

提出了一种新的基于一般散射模型的hybrid Freeman/eigenvalue 分解算法, 用于分析极化合成孔径雷达(PolSAR)数据。文中, 单位矩阵作为体散射模型, 相干矩阵的两个较大特征值对应的特征向量作为表面散射模型和二次散射模型, 并且不需要反射对称条件。新算法有三个优点: 第一, 表面散射和二次散射不需要反射对称条件, 更符合一般散射体的建模; 第二, 因为散射能量是相干矩阵特征值的线性组合, 所以散射能量具有旋转不变性; 第三, 表面散射能量和二次散射能量避免了负值现象。在San Francisco地区的AIRSAR数据上进行了实验, 证明了新算法的有效性。

Abstract

A novel hybrid Freeman/eigenvalue decomposition with general scattering models was proposed for polarimetric synthetic aperture radar (PolSAR) data. A unit matrix represents the volume scattering model, and eigenvectors corresponding to the two larger eigenvalues of the coherency matrix are used as the surface scattering model and double-bounce scattering model for non-reflection symmetry condition. There are three advantages in the proposed hybrid decomposition. Firstly, the surface and double-bounce scattering models are free from the reflection symmetry constraint which is more general and realistic for common media. Secondly, since the scattering powers of the proposed method are solved as linear combinations of the eigenvalues derived from the coherency matrix, they are all roll-invariant parameters. Thirdly, negative powers of surface scattering and double-bounce scattering are avoided with non-rotation of the coherency matrix. Fully PolSAR data on San Francisco are used in the experiments to prove the efficacy of the proposed hybrid decomposition.

参考文献

[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.

[2] 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.

[3] 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.

[4] 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.

[5] Freeman A. Fitting a Two-Component Scattering Model to Polarimetric SAR Data from Forests ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(8): 2583-2592.

[6] Arii M., Zyl J. J. van, Kim Y. J. Adaptive model-based decomposition of polarimetric SAR covariance matrices ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(3): 1104-1113.

[7] Antropov O., Rauste Y. Hme T. Volume scattering modeling in PolSAR decomposition: study of ALOS PALSAR data over Boreal forest ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(10): 3838-3848.

[8] 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.

[9] Lee J. S., Ainsworth T. L. The effect of orientation angle compensation on coherency matrix and polarimetric target decompositions ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1): 53-64.

[10] An W., 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.

[11] Sato A., Yamaguchi Y., Singh G. et al. Four-component scattering power decomposition with extended volume scattering model ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2012, 9(2): 166-170.

[12] Singh G., Yamaguchi Y. Park S. E. General four-component scattering power decomposition using unitary transformation ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(5): 3014-3022.

[13] Cloude S. R. Polarisation: Applications in Remote Sensing ［M］. London, U.K.: Oxford Univ. Press, 2009.

[14] Singh G., Yamaguchi Y., Park S. E., et al. Hybrid Freeman/Eigenvalue decomposition method with extended volume scattering model ［J］. IEEE Geoscience Remote Sensing Letters, 2013, 10(1): 81-83.

[15] Lee J. S., Grunes M. R., Pottier E., et al. Unsupervised terrain classification preserving scattering characteristics ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 722-731.

[16] Berman J. D. B., Sanchez J. M. L. Applying the Freeman–Durden Decomposition Concept to Polarimetric SAR Interferometry ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(1): 466-479.

[17] Sharma J. J., Hajnsek I., Papathanassiou K. P., Polarimetric decomposition over Glacier ice using long-wavelength airborne PolSAR ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(1): 519-535.

[18] Neumann M., Ferro-Famil L., Reigber A. Improvement of vegetation parameter retrieval from polarimetric SAR interferometry using a simple polarimetric scattering model. ［J］.POLinSAR 2009, Rome, Italy, Jan. 2009.

[19] 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.

[20] Cloude, S. R., Group theory and polarization algebra ［J］OPTIK, 1986, 75(1): 26-36.

[21] Lee J. S., Pottier E. Polarimetric Radar Imaging: From Basics to Applications ［M］. Boca Raton, FL: CRC Press, 2009.

[22] http: ∥earth.eo.esa.int/polsarpro/datasets.html［EB OL］.

[23] Lee J S. Improved sigma filter for speckle filtering of SAR imagery ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(1): 202-213.

张爽, 王爽, 焦李成, 陈博, 刘芳, 毛莎莎, 柯熙政. 基于一般散射模型的Hybrid Freeman/Eigenvalue分解算法[J]. 红外与毫米波学报, 2015, 34(3): 265. ZHANG Shuang, WANG Shuang, JIAO Li-Cheng, CHEN Bo, LIU Fang, MAO Sha-Sha, KE Xi-Zheng. A novel hybrid Freeman/eigenvalue decomposition with general scattering models[J]. Journal of Infrared and Millimeter Waves, 2015, 34(3): 265.

基于一般散射模型的Hybrid Freeman/Eigenvalue分解算法

关于本站 Cookie 的使用提示

全站搜索

基于一般散射模型的Hybrid Freeman/Eigenvalue分解算法

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索