Evolutionary Complex-Valued CNN for PolSAR Image Classification

Mengxuan Zhang, Jingyuan Shi, Long Liu, Xilu Wang, Licheng Jiao

Abstract: With the development of deep learning, many PolSAR image classification methods based on deep learning have shown impressive performance. Most of these methods rely on manually designed neural networks, which requires a lot of professional knowledge. In response to this issue, this study proposes a complex-valued convolutional neural network architecture search based on evolutionary algorithm for PolSAR image classification. The complex-valued convolutional neural network can extract the complex-valued characteristics of PolSAR images directly. The architecture search for complex-valued convolutional neural network is transformed into an evolutionary population-based optimization, where classification accuracy is the fitness function. Furthermore, a two-steps selection strategy is designed to reduce the architected complexity while ensuring high classification accuracy. The experimental results on two different PolSAR datasets show that the automatic-designed complex-valued convolutional neural network have superior classification performance.