Deep-Learning-Based Polarimetric Data Augmentation: Dual2Full-Pol Extension

Synthetic aperture radar (SAR) systems can be designed with different polarimetric modalities. Most spaceborne SAR systems acquire dual-polarimetric data to meet various operational requirements. They are designed to capture more information about the Earth's surface than single-pol systems and to cover a wider area than full-pol modalities. Dual-polarimetric data may not be as informative as fully polarimetric images. Several methods exist to augment dual-polarimetric images to take the capabilities of fully polarimetric data. Such methods, nevertheless, are either specific to special dual-pol modalities, i.e., compact modes, or rely on model assumptions that may not be valid in various scattering scenarios. In this article, a new framework for reconstructing fully polarimetric information from typical modalities of dual-pol data is proposed. The framework uses deep learning solutions to augment dual-polarimetric data without relying on model assumptions. Besides the specific architecture of the network used, which makes it efficient to extract distinctive features, a specific loss function is defined to account for the different scattering properties of the polarimetric data. Experiments on different real data show that the reconstruction performance of the proposed framework is superior to the conventional reconstruction method that widely experimented in the literature. Moreover, the pseudo-fully polarimetric data reconstructed by the proposed method closely match the actual fully polarimetric images acquired by radar systems, confirming the reliability and effectiveness of the proposed method.