Semi-Supervised PolSAR Image Classification Based on Deep Co-Training with Superpixel Restrained Strategy.

Deep learning-based PolSAR image classification models have obtained great performance. However, they require large-scale labeled samples for training. Therefore, the deficient labeled samples is a significant challenge. In this paper, we propose a deep co-training network for PolSAR image classification, which introduces the co-training into the deep networks and then both labeled and unlabeled pixels can be used in a semi-supervised way. Firstly, the deep co-training network is established by applying the convolutional neural network and complex-valued 3D convolution neural network as two base classifiers according to the characteristics of PolSAR data. Then a high-confidence sample selection strategy is proposed by applying a super-pixel restrained strategy in the co-training process and the reliability of the selected unlabeled samples are further enhanced. Experimental results show that the proposed method can obtain high classification accuracy with much less labeled samples.