SAR Image Segmentation Using Markov Random Fields
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SAR image segmentation can be implemented using Markov Random Fields (MRF), a probabilistic graphical model particularly effective for image segmentation and classification tasks. The MRF approach models spatial dependencies between pixels through neighborhood systems and energy minimization functions. In practical implementation, this typically involves defining clique potentials and using optimization algorithms like Iterated Conditional Modes (ICM) or Graph Cuts to minimize the energy function. The method allows users to specify any number of categories as input parameters, enabling precise segmentation and classification of SAR images based on statistical dependencies between adjacent pixels. Key implementation aspects include parameter estimation for Gaussian mixture models and proper neighborhood structure definition to capture spatial context effectively.
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