SVMMAP Modeling of SAR Imagery for Unsupervised Segmentation with Bootstrap Sampling

Yan Wei Ju; Yan Zhang

doi:10.4028/www.scientific.net/AMM.614.393

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 614SVMMAP Modeling of SAR Imagery for Unsupervised...

SVMMAP Modeling of SAR Imagery for Unsupervised Segmentation with Bootstrap Sampling

Abstract:

A spatially variant mixture multiscale autoregressive prediction (SVMMAP) model is present, which was applied to segmentation of SAR imagery. General process is as follow: at first, by Bootstrap sampling technique a small representative set of pixels is selected; then, expectation maximization (EM) algorithm and least square (LS) estimation were used to estimate the model, and minimum description length (MDL) rule was employed to choose classification number; at last, Bayes classifier was used to segment image. For a simulated image of size 256×256, a segmentation accuracy of 99.76% was achieved. Besides, quantitative assessment was also presented about segmentation quality of images.

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Periodical:

Applied Mechanics and Materials (Volume 614)

Pages:

393-396

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.614.393

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Online since:

September 2014

Authors:

Yan Wei Ju*, Yan Zhang

Keywords:

EM Algorithm, SAR Imagery, Segmentation

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* - Corresponding Author

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