Abstract
Abstract. 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.
Highlights
A polarimetric synthetic aperture radar (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
We consider the real part of the covariances measured by the PolSAR sensor and an exponential Fisher Vectors (eFV) is derived from a mixture of real Wishart probability distributions functions and Gaussian pdfs
In this paper we present the application of models originated in the computer vision literature to the problem of land cover classification, this is the task of assigning labels to pixels based on the dispersion properties of the objective measured by a Pol-we introduce the fundamental concepts on PolSAR image generation and the eFV image representation
Summary
A polarimetric synthetic aperture radar (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. In the eFV scheme the content of the image (pixels, regions and/or the whole image) is characterized by the standardized gradient vector derived from different mixtures of convenient distributions In this case, we consider the real part of the covariances measured by the PolSAR sensor and an eFV is derived from a mixture of real Wishart probability distributions functions (pdfs) and Gaussian pdfs. The hypotheses of this work are that the theoretical distribution of the data follows a Wishart distribution, with Gaussian distributions competitive results can be obtained and that using other types of similarity measures the results in the classification can be improved
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