Semi-supervised recurrent complex-valued convolution neural network for polsar image classification

This paper presents a novel semi-supervised terrain classification method of polarimetric synthetic aperture radar (PolSAR) image based on complex-valued convolution neural network (CV-CNN). Our proposed method only needs a small number of labeled samples to achieve good classification results. First, a Wishart classifier is used to find highly reliable samples in PolSAR data. Then, a new semi-supervised deep recurrent CV-CNN (RCVCNN) classification model has been proposed to improve PolSAR image classification accuracy and effectively solve network overfitting. Finally, a real PolSAR dataset is used to verify the effectiveness of our algorithm. Compared with the other three state-of-the-art methods, the proposed one show improvements in accuracy and better consistency.