Improved polarization scattering imaging using local-global context polarization feature learning framework

Polarization scattering imaging (PSI) is an extremely challenging problem due to the fact that scattering media can lead to severe degradation of the object information. In this paper, we propose a novel high-performance computational method, the PSI based on a well-designed local-global context polarization feature learning (LGCPFL) framework, named PSI-LGCPFL, to efficiently retrieve the target’s information. In the LGCPFL framework, the dilated convolutional neural network and Swin Transformer are concurrently introduced to capture local polarization feature information and long-range polarization dependencies in the polarization scattering images, respectively. We in-depth investigate the relationship between the geometry of target, the scattering imaging distance, and the constituent materials and the PSI quality. The performance of our proposed PSI-LGCPFL is benchmarked and evaluated on three testing datasets established in real-life scenarios, i.e., STR24, DIS50 and MAT18, which cover untrained target’s geometry, various untrained targets lying in untrained distances between scattering medium and targets, and diverse untrained target’s materials with different background materials, respectively. The experimental results demonstrate that the proposed PSI-LGCPFL has a superior performance on retrieving the target’s information with high-generalization abilities and reasonably inferring speed, and outperforms several existing state-of-the-art methods. To our knowledge, PSI-LGCPFL is the first approach to achieve the PSI by polarization characteristics and a deep learning model with Swin Transformer. It also highlights the prospect of accurately reconstruction of remote sensing target’s information in scattering medium via using polarization information and deep learning