SAR圖像基于Rayleigh分布假設(shè)的最小誤差閡值化分割
RAYLEIGH-DISTRIBUTION BASED MINIMUM ERROR THRESHOLDING FOR SAR IMAGES
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摘要: 針對合成孔徑雷達(dá)(SAR)圖像的特點(diǎn),本文提出基于灰度直方圖的混合偏移Rayleigh分布假設(shè)下的最小誤差閾值化分割算法,并與現(xiàn)有的基于Gauss和Poisson分布假設(shè)下的最小誤差分割算法以及經(jīng)典的Otsu算法作了比較。實(shí)驗(yàn)和Kolmogorov-Smirnov檢驗(yàn)結(jié)果表明對SAR圖像而言,基于Rayleigh假設(shè)的算法可以取得更好的分割效果。Abstract: This paper presents a minimum error thresholding algorithm under the hypothesis that the gray level histogram of SAR image fitting to a mixture model of shifted Rayleigh distribution. This algorithm is applied to real SAR images and compared with traditional Otsu algorithm and other minimum error thresholding algorithms based on various models of histogram. The hypothesis of Rayleigh distribution model is confirmed by Kolmogorov-Smirnov testing and the results obtained show that the proposed new algorithm has good performance in image thresholding for SAR images.
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