提出一种新的基于Contourlet概率分布的边缘检测算法, 首先,对图像Contourlet系数概率分布的混合高斯分布特性进行分析,并建立图像Contourlet 系数大小状态的概率模型,同时对基于该模型的图像线状奇异信号进行分离；其次,改进最大类间方差的阈值选取方法,提出一种基于类间距离和类内方差的阈值选取方法,在保证类间距离最大的同时提高了类内聚合度；最后，利用所选阈值对分离的图像线状奇异信号进行二值化处理,并对边缘信息进行提取。实验结果表明,与传统经典边缘检测方法相比,所提出的边缘检测方法在有效检测出遥感图像中光滑边缘的同时可以对图像中次要的奇异信息进行有效的屏蔽,具有很好的实用性。

We propose a new approach for detecting edges based on Contourlet probability distribution. First we analyze the mixture of Gaussian distribution traits of the Contourlet coefficients. Then we establish the probability model regarding the Contourlet coefficients which can be described by the big state and the small state. At the same time, we separate the linear singular signals on the model of the image. Afterwards, we improve the maximal between-class variance by a threshold selection method based on the variance between different classes and the in-class variance. This can ensure the maximum distance between classes, while simultaneously increasing the degree of polymerization in a class. Furthermore, we use the threshold for the binarization of the separated singular signals and extract the edge message. Compared to traditional methods, our method detects smooth edges in remote sensing images effectively. Meanwhile, it can shield the unimportant singular information, making it is useful for practical applications.