Evaluation of Filters for Envisat Asar Speckle Suppression in Pasture Area

In order to quantify real time pasture biomass from SAR image, regression model between ground measurements of biomass and ENVISAT ASAR backscattering coefficient should be built up. An important prerequisite of valid and accurate regression model is accurate grass backscattering coefficient which, however, cannot be obtained when there is speckle. Speckle noise is the best known problem of SAR images because of the coherent nature of radar illumination imaging system. This study aims to choose better adaptive filter from NEST software to reduce speckle noise in homogeneous pasture area, with little regard to linear feature (e.g. edge between pasture and forest) or point feature (e.g. pond, tree) preservation. This paper presents the speckle suppression result of ENVISAT ASAR VV/VH images in pasture of Western Australia (WA) using four built-in adaptive filters of the NEST software: Frost, Gamma Map, Lee, and Refined Lee filter. Two indices are usually used for evaluation of speckle suppression ability: ENL (Equivalent Number of Looks) and SSI (Speckle Suppression Index). These two, however, are not reliable because sometimes they overestimate mean value. Therefore, apart from ENL and SSI, the authors also used a new index SMPI (Speckle Suppression and Mean Preservation Index). It was found that, Lee filter with window size 7×7 and Frost filter (damping factor = 2) with window size 5×5 gave the best performance for VV and VH polarization, respectively. The filtering, together with radiometric calibration and terrain correction, paves the way to extraction of accurate backscattering coefficient of grass in homogeneous pasture area in WA.