PolSAR(Polarimetric Synthetic Aperture Radar)图像分类的传统方法在前期需要对数据进行特征提取, 涉及较多的人为参与, 且分类精度有待进一步提高。此外, 在采用监督分类方法时, 某些地物存在小样本问题, 针对这些问题并结合PolSAR图像精细分类的需求, 提出基于3D卷积神经网络的PolSAR图像地物精细分类方法, 将传统卷积神经网络扩展为三维并将其应用于PolSAR图像分类中, 利用PolSAR数据多通道特性, 充分挖掘数据中的信息, 提高分类性能, 并采用虚拟样本扩充的方法改善某些地物的小样本情况, 获得更好的分类结果。实验结果表明: 3D卷积神经网络较2D卷积神经网络在PolSAR图像地物精细分类中有较好的性能, 且虚拟样本扩充方法能够有效改善小样本分类问题。

The traditional classification methods of PolSAR image generally required the feature extraction in the early stage, involving more human participation, and the classification accuracy needed further improvement. In addition, when using supervised classification method, there were sometimes small sample problems. In view of these problems and combining the requirement of PolSAR image fine classification, a PolSAR image classification method based on 3D convolution neural network was proposedr. The traditional convolution neural network was extended to three dimensions and applied to PolSAR image classification, and the classification method was described in detail. Thus, the characteristics of the multichannel PolSAR image could be fully excavated and improve the classification performance. Moreover, the method of virtual sample expansion was used to improve the small sample situation of certain category and get better classification results. Experimental results showed that 3D convolution neural network could get better performance than 2D convolution neural network in PolSAR image classification and the virtual sample expansion method could effectively improve the small sample classification problem.