ABSTRACT The inherently complex‐valued nature of polarimetric SAR data necessitates using specialised algorithms capable of directly processing complex‐valued representations. However, this aspect remains underexplored in the deep learning community, with many studies opting to convert complex signals into the real domain before applying conventional real‐valued models. In this work, we leverage complex‐valued neural networks and investigate the performance of complex‐valued convolutional autoencoders. We show that these networks can effectively compress and reconstruct fully polarimetric SAR data while preserving essential physical characteristics, as demonstrated through Pauli, Krogager and Cameron coherent decompositions, as well as the noncoherent decomposition. Finally, we highlight the advantages of complex‐valued neural networks over their real‐valued counterparts. These insights pave the way for developing robust, physics‐informed, complex‐valued generative models for SAR data processing.
Gabot et al. (2026) studied this question.