贝叶斯融合在SAR图像分类中的应用

Application of the Bayesian Fusion in SAR Image Classification

本文研究将数据融合理论用于SAR图像分类 .通过贝叶斯理论进行多通道SAR图像测量级数据融合 ,充分利用像素的从属信息并获得单通道分类无法获取的分类结果 ,有效保留各通道有用信息并抑制图像中的斑点噪声 ;针对贝叶斯融合涉及到的先验概率的问题采用两种方式进行先验概率估计 ,对估计引起的马赛克现象提出了三种解决方法 ;并提出三种先验概率融合方法 。

This paper studies the application of multi-channel fusion in the SAR image classification. In order to make full use of the membership information of pixel to the object classes, we use Bayesian fusion to fuse the information on measurement level. As prior probability of every object class is used in fusion, two methods are used to estimate the probability. The estimation causes a kind of mosaic effect, so modulated Gaussian distribution is introduced to deal with the problem but setting higher threshold to end the iteration proves to be a better method. In order to get an overall prior probability for every object class, three fusion methods of prior probabilities are proposed. Finally, a relative optimal method of multi-channel SAR image classification is achieved with experiments and studies.