A Novel Multiangle Images Association Algorithm Based on Supervised Areas for GNSS-Based InSAR
Zhanze Wang, Feifeng Liu, Runze Shang, Jingtian Zhou
Abstract
Global navigation satellite system-based synthetic aperture radar interferometry (GNSS-based InSAR) systems can achieve 3-D deformation retrieval by associating multiangle images of different satellites. However, the difference in the scene radar cross section (RCS) and resolution cells makes multiangle images vary considerably. In addition, low resolution will further aggravate the difference in multiangle images. In this letter, a multiangle images association algorithm is proposed for GNSS-based InSAR systems. First, the supervised area is introduced to describe the areas of the same deformation based on the persistent scatter (PS) point. Then, the initial multiangle images association results are obtained by overlapping all PS points supervised areas of all satellites. Finally, the associated areas are normalized to obtain valid associated results. The raw data from eight Beidou satellites are used to prove the effectiveness of the proposed algorithm.