Impact of Speckle Filtering on the Decomposition and Classification of Fully Polarimetric RADARSAT-2 Data

Decomposition and classification are vital processing stages in polarimetric synthetic aperture radar (PolSAR) information processing. Speckle noise affects SAR data since backscattered signals from various targets are coherently integrated. Current study investigated the impact of speckle suppression on the target decomposition and classification of RADARSAT-2 fully polarimetric data. Speckle filters should suppress the speckle noise along with the retention of spatial and polarimetric information. The performance of improved Lee–Sigma, intensity-driven adaptive neighborhood (IDAN), refined Lee, and boxcar filters were assessed utilizing the spaceborne dataset, that is, fully polarimetric RADARSAT-2 C-band SAR data for the Mumbai region, India. The effect of speckle suppression on target decomposition was analyzed in this study. Different speckle noise suppression techniques were applied to RADARSAT-2 dataset, followed by Yamaguchi three-component and VanZyl decompositions. The obtained findings revealed that the improved Lee–Sigma filter demonstrated better volume scatterings in forest areas and double bounce in urban areas than the other techniques considered in the analysis. Additionally, the efficacy of the different speckle suppression techniques listed above was assessed. The effectiveness of the speckle filtering algorithm was evaluated by applying the Wishart supervised classification to the filtered and unfiltered data. IDAN, boxcar, refined Lee, and improved Lee–Sigma filters were assessed to find the classification accuracy improvement. A considerable amount of improvement was observed in the classification accuracy for mangrove and forest classes. Minimal enhancement was detected for settlement, bare soil, and water classes.