Bootstrap Techniques for Error Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Bootstrap Coverage Plots for Image Segmentation
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Hierarchical stochastic modeling of SAR imagery forsegmentation/compression
IEEE Transactions on Signal Processing
Multiscale segmentation and anomaly enhancement of SAR imagery
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
A spatially constrained mixture model for image segmentation
IEEE Transactions on Neural Networks
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We propose a new model built on multiscale tree structure, spatially variant mixtures of multiscale autoregressive moving average (SVMMARMA) model, for unsupervised synthetic aperture radar (SAR) imagery segmentation. We derive an expectation maximization (EM) algorithm for learning the pixel labeling as well as the parameters of the component models. We also present the bootstrap sampling technique applied to the parameter estimation, which not only increases estimation precision, but also saves computation time greatly. Finally, we design classifier based on Euclidean distance of multiscale ARMA coefficients. Experiments results show this model gives better results than previous methods.