Paper Info
Title
Least-Squares Estimation for Pseudo Quad-Pol Image Reconstruction from Linear Compact Polarimetric SAR
Abstract
Compact polarimetry is a hybrid dual-polarization imaging mode, which is used to enable wide swath coverages and provide more polarimetric information compared with the conventional dual-polarimetric imaging modes (HH/HV and VH/VV). In applications of compact polarimetric synthetic aperture radar (Pol-SAR), pseudo quad-polarimetric (quad-pol) image reconstruction is an important technique. In this study, we propose a least-squares (LS) based method to estimate the quad-pol covariance elements for the linear π/4 compact polarimetric mode. Different from existing quad-pol reconstruction approaches, which use an iterative approach to refine the model solution based on multi-look data, the LS method uses a set of data points to best fit the reconstruction model and is applicable to both multi-look and single-look complex data. In this study, a decomposition-based three-component reconstruction model is exploited to construct the system of non-linear equations. Then, the minimization problem is addressed in a local window for the cross-polarized term, which is optimized with bound constraints. Furthermore, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m - {\alpha _s}$</tex-math></inline-formula> decomposition for the linear compact mode is developed, which is used to approximate the reconstruction model parameter for the LS model function. Experiments are performed on C-band RADARSAT-2 data collected over agriculture fields, an urban area, and an area with complex terrain types. In comparison with the iterative-based methods, the LS-based reconstruction method shows its superiority in estimating both the cross-polarized term and the co-polarized phase difference.
Year
DOI
Venue
2019
10.1109/JSTARS.2019.2910395
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Keywords
Field
DocType
Image reconstruction,Scattering,Synthetic aperture radar,Mathematical model,Covariance matrices,Imaging,Radar imaging
Iterative reconstruction,Least squares,Computer vision,Remote sensing,Polarimetric sar,Artificial intelligence,Mathematics
Journal
Volume
Issue
ISSN
12
10
1939-1404
Citations 
PageRank 
References 
0
0.34
0
Authors
4
Name
Order
Citations
PageRank
Junjun Yin13211.12
Konstantinos Papathanassiou228037.68
Jian Yang348364.80
Peng Chen403.04