  Polarimetric synthetic aperture radar (PolSAR) images are widely used in
disaster detection and military reconnaissance and so on. However, their
interpretation faces some challenges, e.g., deficiency of labeled data,
inadequate utilization of data information and so on. In this paper, a
complex-valued generative adversarial network (GAN) is proposed for the first
time to address these issues. The complex number form of model complies with
the physical mechanism of PolSAR data and in favor of utilizing and retaining
amplitude and phase information of PolSAR data. GAN architecture and
semi-supervised learning are combined to handle deficiency of labeled data. GAN
expands training data and semi-supervised learning is used to train network
with generated, labeled and unlabeled data. Experimental results on two
benchmark data sets show that our model outperforms existing state-of-the-art
models, especially for conditions with fewer labeled data.
