Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Detection of abrupt changes: theory and application
Detection of abrupt changes: theory and application
Multiplicative noise models: parameter estimation using cumulants
Signal Processing - Special issue on higher order statistics
Detection, Estimation, and Modulation Theory: Radar-Sonar Signal Processing and Gaussian Signals in Noise
Optimal segmentation of random processes
IEEE Transactions on Signal Processing
Analysis of multiscale products for step detection and estimation
IEEE Transactions on Information Theory
On the detection of edges in vector images
IEEE Transactions on Image Processing
Joint segmentation of wind speed and direction using a hierarchical model
Computational Statistics & Data Analysis
Real-Time Model-Based Fault Detection and Isolation for UGVs
Journal of Intelligent and Robotic Systems
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This paper addresses the problem of Bayesian off-line change-point detection in synthetic aperture radar images. The minimum mean square error and maximum a posteriori estimators of the changepoint positions are studied. Both estimators cannot be implemented because of optimization or integration problems. A practical implementation using Markov chain Monte Carlo methods is proposed. This implementation requires a priori knowledge of the so-called hyperparameters. A hyperparameter estimation procedure is proposed that alleviates the requirement of knowing the values of the hyperparameters. Simulation results on synthetic signals and synthetic aperture radar images are presented.