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In this paper, we present a one-class-extraction framework for high resolution Synthetic Aperture Radar (SAR) image classification. The experiment on a TerraSAR-X SAR image shows that the proposed framework provides a promising solution for SAR image classification.
In this paper, we present a number-of-classes-adaptive unsupervised classification framework for synthetic aperture radar (SAR) images. The framework aims at the provision of robust classification for SAR images even if the number of classes existing in the scene is unknown. It mainly consists of estimation of the number of classes, extraction of each class center, classification of image patches,...
In this letter, we present a novel foreground/background separation (FBS) framework for interpreting polarimetric synthetic aperture radar (PolSAR) images. The FBS framework takes the spatial relations between pixels into consideration and incorporates the advantages of pairwise dissimilarity-based grouping schemes. The FBS method can separate specific targets and objects from the background, which...
In this paper, we present a novel urban area extraction method for High Resolution (HR) Synthetic Aperture Radar (SAR) images based on an iterated Foreground/Background Separation (iFBS) framework. The performance of the proposed approach is presented and analyzed on a TerraSAR-X HR SAR experimental data set.
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