中国激光, 2014, 41 (s1): s109011, 网络出版: 2014-07-03  

基于最小类平均绝对偏差算法的遥感图像分割

Remote Sensing Image Segmentation Based on Minimum Class Mean Absolute Deviation
作者单位
北京师范大学信息科学与技术学院, 北京 100875
摘要
针对二维Otsu及其改进算法分割直方图非高斯分布的遥感图像效果较差等问题,提出了一种基于最小类平均绝对偏差的遥感图像分割算法(MCMAD)。利用对角线投影法把遥感图像的二维直方图转化为一维直方图,从而降低计算复杂度;在不同阈值下计算一维直方图相应类中像素出现的概率和类中像素灰度的期望值;遍历一维直方图的所有阈值,得到不同阈值对应的类平均绝对偏差,将最小类平均绝对偏差对应的阈值作为最佳阈值分割点。实验结果表明,与二维Otsu及其改进算法相比,MCMAD算法不仅能够很好的分割直方图为高斯分布的遥感图像,而且改善了直方图为拉普拉斯分布的遥感图像分割效果。此外,新算法的时间消耗也很低。
Abstract
Otsu method and its improved methods are widely suitable for the images whose histograms belong to Gauss distribution. However, they perform poor when the histograms of images belong to a mixture of other distributions. An algorithm based on the minimum class mean absolute deviation (MCMAD) is proposed. The new algorithm transforms two-dimension histogram into one-dimension histogram to decrease the computation complexity by diagonal projection method. The new proposed algorithm calculates the class mean and the class probability of every threshold in one-dimension histogram. The new proposed algorithm gets the minimum class mean absolute deviation of different thresholds by traversing all the thresholds in the one-dimension histogram. Among these thresholds, the threshold corresponding to the minimum class mean absolute deviation is the best segmentation threshold. Experimental results show that the new proposed algorithm not only can segment well on remote sensing images with histograms belonging to normal distribution, but also improve the performance of the remote sensing images with histograms belonging to Laplace distribution comparing with traditional Otsu method and its improved methods. Furthermore, the time consuming of the new proposed algorithm is low.

杨绪业, 李傲雪, 徐帅婧, 张立保. 基于最小类平均绝对偏差算法的遥感图像分割[J]. 中国激光, 2014, 41(s1): s109011. Yang Xuye, Li Aoxue, Xu Shuaijing, Zhang Libao. Remote Sensing Image Segmentation Based on Minimum Class Mean Absolute Deviation[J]. Chinese Journal of Lasers, 2014, 41(s1): s109011.

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