PolSAR Coherency Matrix Optimization Through Selective Unitary Rotations for Model-Based Decomposition Scheme

In this letter, a special unitary SU(3) matrix group is exploited for coherency matrix transformations to decouple the energy between orthogonal states of polarization. This decoupling results in the minimization of the cross-polarization power along with the removal of some off-diagonal terms of coherency matrix. The proposed unitary transformations are utilized on the basis of the underlying dominant scattering mechanism. By doing so, the reduced power from the cross-polarization channel is always concentrated on the underlying dominant co-polar scattering component. This makes it unique in comparison to state-of-the-art techniques. The proposed methodology can be adopted to optimize the coherency matrix to be used for the model-based decomposition methods. To verify this, pioneer three-component decomposition model is implemented using the proposed optimized coherency matrix of two different test sites. The comparative studies are analyzed to show the improvements over state-of-the-art techniques.

[1]  Rajib Kumar Panigrahi,et al.  A fast alternative to three- and four-component scattering models for polarimetric SAR image decomposition , 2017 .

[2]  Yoshio Yamaguchi,et al.  Four-Component Scattering Power Decomposition With Extended Volume Scattering Model , 2012, IEEE Geoscience and Remote Sensing Letters.

[3]  Hiroyoshi Yamada,et al.  Four-Component Scattering Power Decomposition With Rotation of Coherency Matrix , 2011, IEEE Trans. Geosci. Remote. Sens..

[5]  Motoyuki Sato,et al.  Uniform Polarimetric Matrix Rotation Theory and Its Applications , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[6]  Yoshio Yamaguchi,et al.  General Four-Component Scattering Power Decomposition With Unitary Transformation of Coherency Matrix , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[8]  Thomas L. Ainsworth,et al.  Polarimetric SAR data compensation for terrain azimuth slope variation , 2000, IEEE Trans. Geosci. Remote. Sens..

[9]  Avik Bhattacharya,et al.  An Adaptive General Four-Component Scattering Power Decomposition With Unitary Transformation of Coherency Matrix (AG4U) , 2015, IEEE Geoscience and Remote Sensing Letters.

[10]  Jian Yang,et al.  Three-Component Model-Based Decomposition for Polarimetric SAR Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Hiroyoshi Yamada,et al.  A four-component decomposition of POLSAR images based on the coherency matrix , 2006, IEEE Geoscience and Remote Sensing Letters.

[12]  Motoyuki Sato,et al.  General Polarimetric Model-Based Decomposition for Coherency Matrix , 2014, IEEE Trans. Geosci. Remote. Sens..

[13]  E. Pottier,et al.  Polarimetric Radar Imaging: From Basics to Applications , 2009 .

[14]  Ya-Qiu Jin,et al.  Deorientation theory of polarimetric scattering targets and application to terrain surface classification , 2005, IEEE Trans. Geosci. Remote. Sens..

[15]  S. Cloude Polarisation: Applications in Remote Sensing , 2009 .