PCCN: Polarimetric Contexture Convolutional Network for PolSAR Image Super-Resolution

Abstract

Polarimetric synthetic aperture radar (PolSAR) can acquire full-polarization information, which is the solid foundation for target scattering mechanism interpretation and utilization. Meanwhile, PolSAR image resolution is usually lower than the synthetic aperture radar (SAR) image, which may limit its potentials for target detection and recognition. Image super-resolution with the convolutional neural network is a promising solution to fulfill this issue. In order to make full use of both polarimetric and spatial information to further enhance super-resolution performance, this work proposes the polarimetric contexture convolutional network (PCCN) for PolSAR image super-resolution. The main contributions are threefold. First, a new PolSAR data representation of the polarimetric contexture matrix is established, which can fully represent the cube of polarimetric and spatial information into a coded matrix. Then, a dual-branch architecture of the polarimetric and spatial feature extraction block is designed to extract both polarimetric and spatial features separately. Finally, these intrinsic polarimetric and spatial features are effectively fused at both local and global levels for PolSAR image super-resolution. The proposed PCCN method is trained with one X-band polarimetric and interferometric synthetic aperture radar (PiSAR) data, while evaluated with the same scene but different PiSAR imaging direction and with different sensors data including the C-band Radarsat-2 and the X-band COSMO-SkyMed of various imaging scenes. Compared with state-of-the-art algorithms, experimental studies demonstrate and validate the effectiveness and superiority of the proposed method in both visualization examination and quantitative metrics. The proposed method can provide better super-resolution PolSAR images from both polarimetric and spatial viewpoints.