遥感影像K均值聚类中的初始化方法

遥感影像非监督分类对初始点十分敏感。以K均值(K-means)算法为例，利用各种遥感影像实验比较5种初始化方法(随机法、Forgy法、Macqueen法、Kaufman法、MaxMin法)对非监督分类方法的影响。实验表明，Kaufman法相对于其他方法更稳定，获得分类结果更优，适合于各种遥感影像的非监督分类，并指出可以通过采样来加快Kaufman法的运算速度。同时，通过实验分析了采样数和影像区域对初始化方法的影响。

Initialization methods for remote sensing image clustering using K-means algorithm

Unsupervised remote sensing image classification algorithms are very sensitive to the initial conditions. Using the K-means algorithm as an example, the influence of five initialization methods on unsupervised classification algorithms is respectively compared by means of various experiments in remote sensing images. Although K-meansis known for its robustness, it is widely reported in the literature that its performance depends upon initialclustering. A series of experiments are conducted to evaluate the performance of different initialization methods in terms of overall accuracy, Kappa coefficient, initial time and iteration number of convergence. The results of the experiments illustrate that the Kaufman initialization method outperforms the rest of the compared methods as they make the K means more effective and more independent on initial clustering and suggest that the initial time of the Kaufman method can be reduced while maintaining the well results. The convergence speed of the K-means algorithm is also compared using each of the five initialization methods. In addition, the sensitivity of initialization methods in relation to the number of sampling and the image’s size is analyzed.