SAR图像基于Rayleigh分布假设的最小误差阂值化分割
RAYLEIGH-DISTRIBUTION BASED MINIMUM ERROR THRESHOLDING FOR SAR IMAGES
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摘要: 针对合成孔径雷达(SAR)图像的特点,本文提出基于灰度直方图的混合偏移Rayleigh分布假设下的最小误差阈值化分割算法,并与现有的基于Gauss和Poisson分布假设下的最小误差分割算法以及经典的Otsu算法作了比较。实验和Kolmogorov-Smirnov检验结果表明对SAR图像而言,基于Rayleigh假设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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Pal N R, Pal S K. A review on image segmentation techniques[J].Pattern Recognition.1993, 26(9):1277-1294[2]Sahoo P K, et al. A survey of thresholding techniques[J].Computer Vision, Graphics, Image Pro- cessing.1988, 41(2):233-260[3]Gonzales R C, Wintz P. Digital Image Processing. London: Addison-Wesley, 1977, 325-327.[4]Kittler J, Illingworth J. Minimum error thresholding[J].Pattern Recognition.1986, 19(1):41-47[5]Pal N R, Bhandari D. Image thresholding-Some new techniques[J].Signal Processing.1993, 33(2):139-158[6]Zito R R. The shape of SAR histograms[J].Computer Vision, Graphics, Image Processing.1988, 43(3):281-293[7]Otsu N. A threshold selection method from gray-level histograms. IEEE `fans. on SMC, 1979, SMC-9(1): 62-66.[8]Kittler J, Illingworth J. On threshold selection using clustering criterion. IEEE Trans. on SMC, 1985, SMC-15(5): 652-655.[9]Daniel W W. Applied Nonparametric Statistics. 2nd ed., Boston: PWS-KENT Pub., 1990, 319-321. -
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