基于K-means算法分割遥感图像的阈值确定方法研究

当前遥感图像分割中阈值确定方法忽略了前景和背景内在的联系,导致过分割和轮廓模糊,造成整体性能低下。为此,提出一种新型基于K-means算法分割遥感图像的阈值确定方法,通过K-means算法对遥感图像进行分割,利用最大类间方差方法得到分割遥感图像的初始阈值,依据该阈值将遥感图像划分成两类,求出两类的均值,将其作为K-means聚类算法的两个初始聚类中心,通过K-means聚类方法逐次迭代,不断更新聚类中心,直至得到聚类准则函数,从而求出遥感图像的最佳分割阈值。实验结果表明,采用所提方法确定遥感图像分割中的阈值,不仅效率高,而且整体性能优越;将得到的阈值应用于遥感图像分割中,能够使目标和背景被有效分离,且分离后目标部分轮廓比较清晰。

Based on K - means algorithm of remote sensing image threshold segmentation method for determining the research

In current remote sensing image segmentation threshold determination method ignores the foreground and background of inner link, lead to segmentation and contour fuzzy, causing poor performance as a whole.For this, put forward a new kind of remote sensing image segmentation based on K - means algorithm, and a method for determining the threshold value of by K - means algorithm for remote sensing image segmentation, the between-cluster variance method are used to get the initial threshold segmentation of remote sensing image based on the threshold will remote sensing image was divided into two categories, the average of two types, as K - means clustering algorithm of two initial clustering center, through the K - means clustering method of successive iteration, constantly updated clustering center, until get the clustering criterion function, thus the optimal threshold segmentation of remote sensing images.The experimental results show that the proposed method is adopted to define in the remote sensing image segmentation threshold, not only high efficiency, and overall superior performance;Will get the threshold value of application in remote sensing image segmentation, can separate the target and background is effective, and the target after separation section outline clearer.