General Five-Component Scattering Power Decomposition with Unitary Transformation (G5U) of Coherency Matrix

Author:

Malik Rashmi1ORCID,Singh Gulab2ORCID,Dikshit Onkar1,Yamaguchi Yoshio3ORCID

Affiliation:

1. Department of Civil Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India

2. Centre of Studies in Resources Engineering, Indian Institute of Technology Bombay, Mumbai 400076, India

3. Faculty of Engineering, Niigata University, Niigata 950-2181, Japan

Abstract

The polarimetric synthetic aperture radar (PolSAR) provides us with a two-by-two scattering matrix data set. The ensemble averaged coherency matrix in an imaging window derived using a scattering matrix has all non-zero elements in its three-by-three matrix. It is a full 3 × 3 matrix that bears nine real-valued and independent polarimetric parameters inside. In the proposed decomposition method, G5U, we preprocess observed coherency matrix [T] by using two consecutive unitary transformations to become an ideal form for five-component decomposition. The transformation reduces nine parameters to seven, which is the best fit for five-component scattering model expansion. We can retrieve five powers corresponding to surface scattering, double bounce scattering, volume scattering, oriented dipole scattering, and compound dipole scattering, directly. These powers can be calculated easily and used to display superb polarimetric RBG images as never before, and are further applicable for polarimetric calibration, classification, validation, etc.

Funder

Department of Science and Technology

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference18 articles.

1. Lee, J.S., and Pottier, E. (2009). Polarimetric Radar Imaging from Basics to Applications, CRC Press.

2. Yamaguchi, Y. (2020). Polarimetric SAR Imaging: Theory to Applications, CRC Press.

3. A three-component scattering model for polarimetric SAR data;Freeman;IEEE Trans. Geosci. Remote Sens.,1998

4. Fitting a Two-Component Scattering Model to Polarimetric SAR Data from Forests;Freeman;IEEE Trans. Geosci. Remote Sens.,2007

5. A four-component decomposition of POLSAR images based on the coherency matrix;Yamaguchi;IEEE Geosci. Remote Sens. Lett.,2006

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