Polsar Image Classification Using Different Codifications Based On Fisher Vectors

A PolSAR is an active sensing device capable of providing images that are robust against variations of weather and atmosphere conditions, irrespective of the time of the day they were acquired. For an efficient use of these images it is necessary to have algorithms capable of classifying these images to generate maps with their content automatically. This paper presents the extension of a PolSAR image classification method based on exponential Fisher Vectors, a Potts smoothing model and different similarity measures. With the proposed extension, improvements in classification with respect to the base method are achieved. Future work consists in extending the codification so as not to have to discard the imaginary part of the data.