结合最大似然算法和波利亚罐模型的全色遥感图像分类

最大似然（ML）算法是一种应用广泛的遥感图像监督分类方法，该算法对样本区域的选择有很高的精度要求，选择难度降低了算法的效率。为此，本文提出了一种结合ML算法和波利亚罐模型的全色遥感图像分类方法。首先由ML算法得到各像素分属各类别的概率，根据得到概率计算此像素的罐模型中不同颜色小球的数量，完成图像罐模型的建立；根据波利亚罐模型随机采样过程，结合邻域，更新中心像素的罐模型中各类颜色小球的组成，直到各类小球数量比例达到稳定，得到最终分类结果。该方法可以进一步精确地对图像进行分类，且对样本选择无要求，简化了分类过程；分别对合成图像和真实遥感图像进行了试验，取得了较好的试验结果；定性和定量分析结果验证了该方法的可行性及有效性。

Panchromatic Remote Sensing Image Classification Combining Maximum Likelihood Algorithm and Polya Urn Model

Maximum likelihood (ML) algorithm is a widely used supervised method for remote sensing images classification.In ML algorithm,samples selections need to be extremely accurate,which also reduce the algorithmic efficiency.Therefore,a new method combined ML algorithm and Polya urn model to achieve panchromatic remote sensing images classification is presented.First,ML algorithm is used to calculate subordination probabilities of pixels.The numbers of balls of different colors are calculated by these probabilities,and the urn model of the image can be initialized.Urns' compositions are iteratively updated by the random sampling process of Polya urn model.The balls of the neighborhood are also combined to determine the next state of the urn.Finally,by steadying the quantity proportions of balls the final classification is achieved.The proposed method could classify images more precisely,and there is no request of samples selections;random selections of samples are practicable,which make the classification process simple.The results obtained on both synthesized and real remote sensing images show that the proposed method works well and is very promising.