SAR影像极化特征的混合高斯模型与分类

Gaussian Mixture Model and Classification of Polarimetric Features for SAR Images

针对高分辨率极化合成孔径雷达(SAR)影像中极化特征呈现尖峰拖尾等复杂多样的统计特点,采用混合高斯模型(GMM)对极化特征进行建模,提出了一种约束距离的混合多元高斯分布的参数估计算法。该参数估计算法在贪婪期望最大算法框架下设计约束距离函数,自动估计混合分量的个数和模型参数,进而在贝叶斯框架下实现极化SAR影像的地物分类。对Radarsat-2旧金山等地区三组影像数据的分类结果表明:相比于经典的分类算法,所提GMM分类算法的总体精度提高了7%～10%,且对样本数目的依赖性更小,在城区和耕地区域等异质区域可以得到精度更高的分类结果。

Aiming at the various statistical characteristics such as peak tailing presented in the high-resolution synthetic aperture radar(SAR) images, we model the polarimetric features according to the Gaussian mixture model(GMM) and come up with a constrained distance estimation algorithm for the parameters of multivariate Gaussian mixture distribution. Under the framework of greedy expectation maximum algorithm, a constraint distance function is designed and the number of mixed components and model parameters are automatically estimated in this parameter estimation algorithm. Consequently the classification of polarimetric SAR images is realized under the Bayesian framework. The classification results of three groups of image data from Radarsat-2 in San Francisco and other places indicate that the proposed GMM classification algorithm possesses an overall accuracy higher by 7 %-10 % comparing with those by the classical classification algorithms. Moreover, its dependence on sample number is small. The more accurate classification results can be obtained in heterogeneous regions such as urban and farmland.