PolSAR image classification via a novel semi-supervised recurrent complex-valued convolution neural network

Abstract

Due to that polarimetric synthetic aperture radar (PolSAR) data suffers from missing labeled samples and complex-valued data, this article presents a novel semi-supervised PolSAR terrain classification method named recurrent complex-valued convolution neural network (RCV-CNN) which combines semi-supervised learning and complex-valued convolution neural network (CV-CNN). The proposed method only needs a small number of labeled samples to achieve good classification results. First, a Wishart classifier is used to select some reliable PolSAR samples. Then, two new semi-supervised deep classification model RCV-CNN1 and RCV-CNN2 have been proposed to improve PolSAR image classification accuracy. Moreover, our proposed methods could solve the problem of network overfitting phenomenon to some extend when the number of training samples is too small. Finally, three real PolSAR dataset are applied to verify the effectiveness of our algorithms. Compared with the other five state-of-the-art methods, the proposed RCV-CNN1 and RCV-CNN2 classification models show good performance in accuracy and generalization.