  Convolutional neural networks (CNNs) have shown good performance in
polarimetric synthetic aperture radar (PolSAR) image classification due to the
automation of feature engineering. Excellent hand-crafted architectures of CNNs
incorporated the wisdom of human experts, which is an important reason for
CNN's success. However, the design of the architectures is a difficult problem,
which needs a lot of professional knowledge as well as computational resources.
Moreover, the architecture designed by hand might be suboptimal, because it is
only one of thousands of unobserved but objective existed paths. Considering
that the success of deep learning is largely due to its automation of the
feature engineering process, how to design automatic architecture searching
methods to replace the hand-crafted ones is an interesting topic. In this
paper, we explore the application of neural architecture search (NAS) in PolSAR
area for the first time. Different from the utilization of existing NAS
methods, we propose a differentiable architecture search (DAS) method which is
customized for PolSAR classification. The proposed DAS is equipped with a
PolSAR tailored search space and an improved one-shot search strategy. By DAS,
the weights parameters and architecture parameters (corresponds to the
hyperparameters but not the topologies) can be optimized by stochastic gradient
descent method during the training. The optimized architecture parameters
should be transformed into corresponding CNN architecture and re-train to
achieve high-precision PolSAR classification. In addition, complex-valued DAS
is developed to take into account the characteristics of PolSAR images so as to
further improve the performance. Experiments on three PolSAR benchmark datasets
show that the CNNs obtained by searching have better classification performance
than the hand-crafted ones.
