Pseudo Quad-Pol Simulation From Compact Polarimetric SAR Data via a Complex-Valued Dual-Branch Convolutional Neural Network

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Compact polarimetry (CP) has attracted much attention in recent years due to its hybrid dual polarization imaging mode. CP synthetic aperture radar (SAR) has a larger swath and can provide more polarimetric information compared with the traditional dual polarization imaging mode (HH/HV or VH/VV). Pseudo quad-polarimetric (quad-pol) data simulation is an important technology in the application of CP data. The goal of pseudo quad-pol data simulation from CP data is to change the form of CP data to the form of quad-pol data without increasing any new information. In this work, a new pseudo quadpol data simulation method from the CP data is proposed. This method combines a complex-valued dual-branch convolutional neural network (CV-DBCNN) to achieve the simulation of the pseudo quad-pol data. It utilizes complex-valued convolutional layers and complex-valued activation function to fully extract the polarimetric information embedded in the complex-valued CP data. For the CV-DBCNN, the branch with 11 kernel size is used to nonlinearly and self-adaptively combine the channel of input data, the branch with 33 kernel size is used to extract the discriminative regional polarimetric features. Furthermore, polarimetric decomposition is utilized to evaluate the scattering mechanisms of the pseudo simulated quad-pol data. Three state-of-the-art methods are utilized for comparison. In comparison with other methods, our proposed reconstruction method based on the CVDBCNN shows its superiority in terms of the pseudo quad-pol data reconstruction and scattering mechanism preservation.