Evaluation of ICA-Based ICTD for PolSAR Data Analysis Using a Sliding Window Approach: Convergence Rate, Gaussian Sources, and Spatial Correlation

Polarimetric incoherent target decomposition aims at accessing physical parameters of illuminated scatters through the analysis of the target coherence or covariance matrix. In this framework, independent component analysis (ICA) was recently proposed as an alternative method to eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high-resolution synthetic aperture radar systems). Until now, the two main drawbacks reported of the aforementioned method are the greater number of samples required for an unbiased estimation, when compared to the classical eigenvector decomposition, and the inability to be employed in scenarios under the Gaussian clutter assumption. In this paper, both drawbacks are analyzed. First, a Monte Carlo approach is performed in order to investigate the bias in estimating Touzi's target-scattering-vector-model parameters when ICA is employed. Simulated data and a RAMSES X-band image acquired over Bretigny, France, are taken into consideration to investigate the bias estimation under different scenarios. Finally, the performance of the algorithm is also evaluated under the Gaussian clutter assumption and when spatial correlation is introduced in the model.

Published in:
Ieee Transactions On Geoscience And Remote Sensing, 54, 7, 4262-4271
Piscataway, Institute of Electrical and Electronics Engineers

 Record created 2016-07-19, last modified 2018-03-17

Rate this document:

Rate this document:
(Not yet reviewed)