NDLI: SAR Image Segmentation Based on Mixture Context and Wavelet Hidden-Class-Label Markov Random Field

Content Provider	IEEE Xplore Digital Library
Author	Ming Li Yan Wu Shunjun Wu
Copyright Year	2007
Description	Author affiliation: Xidian Univ., Xian (Ming Li; Yan Wu; Shunjun Wu)
Abstract	Because of the property that synthetic aperture radar (SAR) image includes plenty of multiplicative speckle noise, a new segmentation algorithm is proposed based on the mixture context and the wavelet hidden-class-label Markov random field (MRF). In our paper, a wavelet mixture heavy-tailed model is constructed, then the hidden-class-label MRF is extended to the wavelet domain to suppress the effect of speckle noise. Since the multi-scale segmentation with overlapping window is presented here, this segmentation method is utilized at the finest scale of stationary wavelet transform (SWT) domain, and the classical segmentation method is also utilized at the coarse scales of discrete wavelet transform (DWT) domain. Finally, a mixture context model is proposed to estimate inter-scale parameters and the optimal segmentation result is derived from a new maximum a posteriori (MAP) classification. The experimental results show that our method achieves accurate SAR image segmentation result and preserves detailed boundary effectively.
Starting Page	360
Ending Page	365
File Size	499659
Page Count	6
File Format	PDF
ISBN	9780769528748
DOI	10.1109/FSKD.2007.505
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-08-24
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image segmentation Wavelet domain Signal processing algorithms Hidden Markov models Speckle Gaussian distribution Discrete wavelet transforms Synthetic aperture radar Markov random fields Wavelet coefficients
Content Type	Text
Resource Type	Article

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