MRF Model-based Change Detection Algorithm Simulation

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Change detection algorithm simulation based on MRF model - MRF modeling for SAR denoising, followed by change detection implementation using Expectation-Maximization (EM) algorithm

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This paper presents a simulation of a change detection algorithm based on the Markov Random Field (MRF) model. The implementation first applies MRF modeling to Synthetic Aperture Radar (SAR) denoising, where image modeling and optimization techniques effectively reduce noise impact. Key functions typically involve constructing energy functions with neighborhood systems and implementing optimization through algorithms like Iterated Conditional Modes (ICM) or Graph Cuts. Subsequently, the Expectation-Maximization (EM) algorithm processes the denoised images to achieve change detection and localization. The EM algorithm implementation generally includes iterative expectation steps (probability estimation of change regions) and maximization steps (parameter updating for change models). This algorithmic approach enhances image processing accuracy and efficiency, providing robust support for research and applications in remote sensing image processing.

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