عنوان مقاله [English]
Due to numerous capabilities of polarimetric radar images as a source of strategic information, their use in military and commercial applications is rapidly growing. One of the important topics of interest to researchers is the classification of these images. So in this paper, the structure of an ensemble of classifiers based on sparse representation technique is presented which classifies adaptively two sets of border and non-border pixels in a polarimetric image. In this plan, various aspects such as base classifiers and their diversity, ensemble of classifier structure, reliability, combination rule, polarimetric and texture features and contextual information are considered. For designing the combination rule of base classifiers, a method based on reliability parameter is proposed. Also, a post-processing stage is used for reducing the unwanted discretion in the output of the ensemble of classifiers. Implementation of the proposed algorithms on a benchmark PolSAR image, demonstrates their superiority over that of traditional techniques.
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