一种改进的舰船合成孔径雷达图像分割方法

An improved ship synthetic aperture radar image segmentation method

摘要:

针对舰船合成孔径雷达(SAR)图像识别中的图像分割问题，运用数理统计领域的方法，以舰船合成孔径雷达图像为研究对象，在深入分析经典K–Means聚类算法以及高斯混合模型之后，提出一个改进的高斯混合模型，用来对舰船合成孔径雷达图像进行分割。该方法采用马氏距离对经典K–Means方法进行改进，同时，将传统高斯混合模型的每一个概率分布，进一步再细分成单个的概率成分，在辅助变量计算过程中，采用梯度上升算法。仿真实验结果显示，研究得到了比使用经典K–Means算法和普通高斯混合模型的分割方法精确度更高、稳定性更好的分割结果。

Abstract:

Aiming at the image segmentation problem in ship Synthetic Aperture Radar(SAR) image recognition, the method of mathematical statistics is utilized to study the ship SAR image. After analyzing classical K–Means clustering algorithm and Gaussian Mixture Model(GMM), an improved Gaussian mixture model is proposed to segment ship synthetic aperture radar images. The method adopts the Mahalanobis distance to improve classical K–Means method. At the same time, each probability distribution of traditional GMM is further subdivided into individual probability components. In the calculation of auxiliary variables, a gradient ascent algorithm is applied. The experimental results show that the segmentation results obtained by this study are more accurate and more stable than the segmentation method using the classic K–Means algorithm and ordinary Gaussian mixture model.