Fine classification of polarimetric SAR images based on 3D convolutional neural network

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.