FULLY CONVOLUTIONAL SEMI-SUPERVISED GAN FOR POLSAR CLASSIFICATION

We propose a novel semi-supervised fully convolutional network for Polarimetric synthetic aperture radar (PolSAR) terrain classification. First, by designing a fully convolutional structure, we can perform pixel-based classification tasks. Then, by applying semi-supervised generative adversarial networks (GANs), we utilize both labeled and unlabeled samples and aim to obtain higher classification accuracy. Through a mini-max two-player game, GAN has better performance than other "single-player" classifiers. Finally, we combine the fully convolutional structure with the semi-supervised GAN. Our fully convolutional semi-supervised GAN (FC-SGAN) has excellent spatial feature learning ability and can perform end-to-end pixel-based classification tasks. Experimental results show that compared with existing works, the proposed method has better performances. Even when the training set gets smaller, our method keeps high accuracy.