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Scattering identification ambiguity has significant effects in the polarimetric SAR (Synthetic Aperture Radar) decomposition and can be reduced by discriminating the permanent and time-varying features using PolInSAR (Polarimetric SAR Interferometry) coherence. PolInSAR based polarimetric decomposition using cosine-squared distribution (PCSD decomposition) is an algorithm that demonstrates the incorporation of PolInSAR coherence in the decomposition model by assuming the distribution of forest structure and vegetation as cosine-squared. This study presents a multifrequency analysis of PCSD decomposition to evaluate its potential to rightly represent the scatterer by reducing the identification ambiguity. The false-color composites (FCC) of PCSD decomposed images are presented to examine the results. A comparison study is also conducted between the PCSD decomposition and the Yamaguchi-4 component (Y4C) decomposition to demonstrate the improvement in the representation of the ground targets. [ABSTRACT FROM AUTHOR]

The characterization of ground targets from a remotely sensed Synthetic Aperture Radar (SAR) image is addressed by polarimetric decomposition. The polarimetric SAR (PolSAR) decomposition measures the contribution of total backscatter from different scattering mechanisms using SAR images. The ambiguities present in the retrieval of scattering are the major problems associated with the model-based decomposition which could be reduced with a combination of interferometric coherence and PolSAR backscatter. The objective of this study is to improve the polarimetric decomposition model for identifying the scattering mechanisms based on the Polarimetric SAR Interferometry (PolInSAR) coherence for natural and manmade features. In this paper, we have proposed a model-based polarimetric decomposition using PolInSAR decorrelation. The PolInSAR decorrelation is exploited here to distinguish the time-varying and invariant scatterers present in the ground. The volume scattering power was calculated using the proposed decorrelation parameter which is the combination of PolInSAR coherence and decorrelation. The proposed algorithm has been tested on spaceborne multifrequency SAR datasets consisting of X-band TerraSAR-X and TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-X), C-band Radarsat-2, and phased array L-band synthetic aperture radar-2 (PALSAR-2) data of advanced land observing satellite-2 (ALOS-2) PolInSAR data for the Dehradun region, India. The results show that there is a remarkable reduction in the ambiguities present in the identification of the scattering mechanism from the SAR image by using the proposed decorrelation-based decomposition model. Moreover, the algorithm is tested on X-band TerraSAR-X and TanDEM-X data of the Haridwar area and Rudrapur area, Uttarakhand, India to analyse the potential of the proposed decomposition technique in representing different manmade and natural features. [ABSTRACT FROM AUTHOR]