Real- and Complex-Valued Neural Networks for SAR Image Segmentation Through Different Polarimetric Representations

In this paper, we investigated the semantic segmentation of Polarimetric Synthetic Aperture Radar (PolSAR) using Complex-Valued Neural Network (CVNN). Although the use of the coherency matrix is ubiquitous as the input of CVNN [1]–[7], Pauli vector is also a relevant representation despite the noise. Two equivalent networks Complex-Valued Fully Convolutional Neural Network (CV-FCNN) and Real-Valued Fully Convolutional Neural Network (RV-FCNN), equivalence in terms of trainable parameters, are compared using both Pauli vector and the coherency matrix as the input feature. Experimentation on San Francisco dataset illustrated a better accuracy of CV-FCNN against its real-valued equivalent.