Performance evaluation of several adaptive speckle filters for SAR imaging

Speckle noise is a significant disturbing factor for SAR image processing. In this study the performance of eight adaptive speckle filter algorithms (Lee, Enhanced Lee, Frost, Enhanced Frost, Gamma, Kuan, Local Sigma and Bit Errors) with several moving window sizes was compared. A bi-polarized ALOS/PALSAR image covering an area in the north of Minas Gerais, Brazil, was used for this study. Three criteria were evaluated to test the ability of the filters to reduce speckle noise (Standard deviation To Mean ratio) and preserve the mean of a homogeneous land cover segment (Normalized Mean), and at the same time their ability to retain detailed edge information (Edge Index). In general, all speckle filters were able to preserve the mean of the homogeneous land cover segments to a satisfactory level. In particular the Enhanced Lee, Frost, Enhanced Frost and Gamma filter with a 7x7 window size were able to significantly suppress speckle noise, with speckle reduction rates up 70%. For the preservation of the edges again the Enhanced Lee, Frost, Enhanced Frost and Gamma filter performed best in the HH-polarized image, even showing enhancement of the edges. For the HV-polarized image most speckle filter algorithms resulted in slight edge blurring.