Adaptive Three-component Decomposition Approach for Polarimetric SAR Data

doi:10.11947/j.AGCS.2016.20150533

Abstract

Abstract: In this paper, the problems such as negative power and scattering mechanism ambiguity in original polarimetric SAR three-component decomposition are introduced, and the remaining flaws in its improved approaches are in depth analyzed. Based on these, an adaptive three-component decomposition is proposed, and more generalized scattering models are used. Because in one pixel there may exist two odd or double bounce scattering targets with different orientation angels, the proposed method firstly considers this situation, so that the surface and double bounce scattering can be preserved more sufficiently. And then the alpha parameter is used to identify the dominant scattering except for the volume scattering. Lastly, an optimization measure to the pixels with negative power is proposed, which significantly decreases the negative power pixels count, so the decomposition will be more accurate and more valid. The results show great improvements in real scattering characteristics extraction and the flaws in model based decomposition approaches can be better resolved.

Key words: polarimetric synthetic aperture radar, polarimetric decomposition, model-based, generalized scattering mechanism, adaptive

CLC Number:

P235

CAI Yongjun, ZHANG Xiangkun, JIANG Jingshan. Adaptive Three-component Decomposition Approach for Polarimetric SAR Data[J]. Acta Geodaetica et Cartographica Sinica, 2016, 45(9): 1089-1095.

References

[1] 王文光. 极化SAR信息处理技术研究[D]. 北京: 北京航空航天大学, 2007. WANG Wenguang. Studies on Information Processing Technique of Polarimetric SAR[D]. Beijing: Beihang University, 2007.
[2] 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.
[3] 郎丰铠, 杨杰, 赵伶俐, 等. 基于Freeman散射熵和各向异性度的极化SAR影像分类算法研究[J]. 测绘学报, 2012, 41(4): 556-562. LANG Fengkai, YANG Jie, ZHAO Lingli, et al. Polarimetric SAR Data Classification with Freeman Entropy and Anisotropy Analysis [J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(4): 556-562.
[4] 刘修国, 姜萍, 陈启浩, 等. 利用改进三分量分解与Wishart分类的极化SAR图像建筑提取方法[J]. 测绘学报, 2015, 44(2): 206-213. DOI: 10.11947/j.AGCS.2015.20130535. LIU Xiuguo, JIANG Ping, CHEN Qihao, et al. Buildings Extraction from Polarimetric SAR Image Using Improved Three-component Decomposition and Wishart Classification[J]. Acta Geodaetica et Cartographica Sinica, 2015, 44(2): 206-213. DOI: 10.11947/j.AGCS.2015.20130535.
[5] AN Wentao, CUI Yi, YANG Jian. Three-component Model-based Decomposition for Polarimetric SAR Data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2010, 48(6): 2732-2739.
[6] CHEN Siwei, SATO M. General Polarimetric Model-based Decomposition for Coherency Matrix[C]//Proceedings of IEEE International Geoscience and Remote Sensing Symposium. Munich: IEEE, 2012: 99-102.
[7] SINGH G, YAMAGUCHI Y, PARK S E, et al.Hybrid Freeman/Eigenvalue Decomposition Method with Extended Volume Scattering Model[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(1): 81-85.
[8] WANG Chunle, YU Weidong, WANG R, et al.Comparison of Nonnegative Eigenvalue Decompositions with and without Reflection Symmetry Assumptions[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(4): 2278-2287.
[9] LEE J S, AINSWORTH T L, WANG Yanting. Generalized Polarimetric Model-based Decompositions Using Incoherent Scattering Models[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 52(5): 2474-2491.
[10] 陈启浩, 刘修国, 黄晓东, 等. 一种极化SAR协方差矩阵综合四分量分解模型[J]. 武汉大学学报(信息科学版), 2014, 39(7): 873-877. CHEN Qihao, LIU Xiuguo, HUANG Xiaodong, et al.An Integrated Four-component Model-based Decomposition of Polarimetric SAR with Covariance Matrix[J]. Geomatics and Information Science of Wuhan University, 2014, 39(7): 873-877.
[11] 张海剑, 杨文, 邹同元, 等. 基于四分量散射模型的多极化SAR图像分类[J]. 武汉大学学报(信息科学版), 2009, 34(1): 122-125. ZHANG Haijian, YANG Wen, ZOU Tongyuan, et al.Classification of Polarimetric SAR Image Based on Four-component Scattering Model[J]. Geomatics and Information Science of Wuhan University, 2009, 34(1): 122-125.
[12] 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.
[13] 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.
[14] CUI Yi, YAMAGUCHI Y, YANG Jian, 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.
[15] ARII M, VAN ZYL J J, KIM Y. Adaptive Model-based Decomposition of Polarimetric SAR Covariance Matrices[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(3): 1104-1113.
[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.
[17] CHEN Siwei, OHKI M, SHIMADA M, et al.Deorientation Effect Investigation for Model-based Decomposition over Oriented Built-up Areas[J]. IEEE Geoscience and Remote Sensing Letters, 2013, 10(2): 273-277.
[18] 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.
[19] XU Feng, JIN Yaqiu. 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. Polarisation. Applications in Remote Sensing[M]. Oxford: Oxford University Press, 2009.