Recursive Convolution Neural Network for PolSAR Image Classification

Considering the limited of the training samples and the effect of speckle noise in PolSAR images, which further affects the learning performance of the classifier, a recursive convolution neural network model (CNN) is proposed. Samples with high confidence of each classification result will be used as the training samples of the next training. Then, a semi-supervised model is obtained for PolSAR image classification. This model is independent of the dependence of supervised classification on manual calibration samples. Furthermore, the model is an end-to-end classification framework based on discriminative feature learning which can learn the spatial texture features of polarized SAR images automatically while performing convolution operations in CNN. In addition, this model tries to learn features that are beneficial to classification from highly confident samples. There are three advantages of the proposed model: firstly, the problem of small samples is solved by increasing the training samples from each iterative classification result. Secondly, the low confidence samples are removed in each iteration to reduce the impact of noise samples on the robustness of the model. Finally, the initialization of CNN parameters in each iteration process is based on the results of the previous learning. As a result, the parameters will be set more and more robustly, so that the entire model will not have poor performance due to random initialization.