Speckle reduction and restoration of synthetic aperture radar data with an adoptive Markov random field model

Citation

MahdianPari M, Motagh M & Akbari V (2013) Speckle reduction and restoration of synthetic aperture radar data with an adoptive Markov random field model. In: IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, 22.07.2012-27.07.2012. New Jersey: IEEE. https://doi.org/10.1109/igarss.2012.6351584

Abstract
This paper proposes a novel speckle reduction method that combines an advanced statistical distribution with spatial contextual information for SAR data. The method for despeckling is based on a Markov random field (MRF) that integrates a K-distribution for the SAR data statistics and a Gauss-MRF model for the spatial context. These two pieces of information are combined based on weighted summation of pixel-wise and contextual models. This not only preserves edge information in the image, but also improves signal-to-noise ratio (SNR) of the despeckled data. Experiments on real SAR data demonstrate the effectiveness of the algorithm compared with well-known despeckling methods.

People

Dr Vahid Akbari

Lect in Artificial Intelligence/Data Sci, Computing Science and Mathematics - Division