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

Abstract

Compact polarimetry (CP) has attracted much attention in recent years due to its hybrid dual-polarization imaging mode. CP synthetic aperture radar 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 reconstruction is an important technology in the application of CP data. The goal of pseudo quad-pol data reconstruction 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 article, a new pseudo quad-pol data reconstruction method from the CP data is proposed. This method combines a complex-valued dual-branch convolutional neural network (CV-DBCNN) to achieve the reconstruction of the pseudo quad-pol data. It utilizes complex-valued convolutional layers and a complex-valued activation function to fully extract the polarimetric information embedded in the complex-valued CP data. For the CV-DBCNN, the branch with 1×1 kernel size is used to nonlinearly and self-adaptively combine the channel of input data, and the branch with 3×3 kernel size is used to extract the discriminative regional polarimetric features. Furthermore, polarimetric decomposition is utilized to evaluate the scattering mechanisms of the pseudo 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 CV-DBCNN shows its superiority in terms of the pseudo quad-pol data reconstruction and scattering mechanism preservation.

Keywords

• Compact polarimetry (CP)
• complex-valued dual-branch convolutional neural network (CV-DBCNN)
• deep learning
• pseudo quad-polarization (quad-pol) data reconstruction
• synthetic aperture radar (SAR)