Revisiting an Iterative Speckle Filtering Technique

Because of speckle noise, estimating extended target scattering properties from Single Look Complex PolSAR data is a difficult problem. Speckle filtering is aiming at reducing noise within extended targets while preserving point target, polarimetric signatures and meaningful details. However, optimal speckle filtering requires some knowledge about the underlying target properties. Also, using all the terms of the quadpol polarimetric matrix in order to drive speckle filtering is still an open problem. Deep Learning techniques have been successful in tackling difficult computer vision tasks and show promising results when applied to SAR and PolSAR data. The goal of this paper is to investigate the use of Convolutional Neural Networks (CNN) in order to extract information from the full quadpol covariance matrix and help improve speckle filtering. Experiments are conducted using simulated PolSAR data and results show that we can potentially recover information from the off-diagonal terms.