Speckle Filtering and its Influence on the Decomposition and Classification of Hybrid Polarimetric Data of RISAT-1

Abstract

Decomposition and classification are crucial steps in the PolSAR (polarimetric synthetic aperture radar) data processing. Speckle noise influences synthetic aperture radar data because of the coherent integration of back scattered signals from various targets. This study examined the influence of speckle noise filtering on the classification and decomposition of hybrid polarimetric data of Radar Imaging Satellite-1 (RISAT-1). A despeckling algorithm should suppress speckle noise and simultaneously maintain the polarimetric data and spatial features. Performances of the boxcar, Lee-sigma, intensity-driven adaptive-neighborhood (IDAN), and refined Lee filters were evaluated using a spaceborne dataset. RISAT‐1 C‐band hybrid polarimetric data of the Mumbai region, India, was utilized. This study addressed the influence of speckle filtering on decomposition. Various speckle suppression techniques were applied on the RISAT-1 dataset, and the m−χ decomposition was subsequently conducted on the dataset. The obtained results indicated that the IDAN filter exhibited a superior improvement in double-bounce scattering in the urban area and in diffuse scattering in the forest area compared with the other filters considered in this study. Furthermore, the effectiveness of various speckle filters was evaluated. The Wishart supervised classification was applied on the filtered and unfiltered data to evaluate the effectiveness of the speckle suppression algorithm. The refined Lee, boxcar, Lee-sigma, and IDAN filters were evaluated for improving their classification accuracy. The classification accuracy of the settlement and mangrove classes was considerably improved. A marginal improvement was observed for the forest and bare soil classes. The improvement in the classification accuracy of the water class was insignificant.