Adaptive Local Means Filter for Polarimetric SAR Images; Despeckling for Homogeneous and Heterogeneous Clutter Models

Polarimetric radar images suffer from the presence of speckles that degrade the received signal and introduce untruthful indications about the nature of the objects. In this study, we proposed a new framework to filter polarimetric images in which the edges and the channel correlation are preserved. Through a proposed scheme, the image is segmented into groups of regular and irregular pixels. The segmentation process is based on the homogeneity of the texture variation throughout the image. In the homogeneous area, speckle reduction is performed using the adaptive local mean of the neighboring pixels. For non-homogeneous surfaces, the scheme works independently for each set of resolution cells using the general product model containing both intensity and texture information. Quantitative and qualitative assessments confirmed that the proposed filter achieved highly ranked order; it has the ability to preserve fine details, polarimetric information, and to maintain the scattering mechanism of the different objects.