Reconstruction Full-Pol SAR Data from Single-Pol SAR Image Using Deep Neural Network

Compared with single channel polarimetric (single-pol) SAR image, full polarimetric (full-pol) data convey richer information, but with compromises on higher system complexity and lower resolution or swath. In order to balance these factors, a deep neural networks based method is proposed to recover full-pol data from single-pol data in this paper. It consists of two parts:a feature extractor network is applied first to extract hierarchical multi-scale spatial features, followed by a feature translator network to predict polarimetric features with which full-pol SAR data can be recovered. Both qualitative and quantitative results show that the recovered full-pol SAR data agrees well with the real full-pol data. No prior information is assumed for scatterer media, and the framework can be easily expanded to recovery full-pol data from non-full-pol data. Traditional PolSAR applications such as model-based decomposition and unsupervised classification can now be applied directly to recovered full-pol SAR image to interpret the physical scattering mechanism.