Automatic Extraction of 3-D Ground Control Points Using Stereo SAR from China’s GF3B and SVN2-01/02

The extraction of ground control points (GCPs) from spaceborne synthetic aperture radar (SAR) is an effective technique for rapid, contactless, and globally available georeferencing. In recent years, China has made significant progress in the deployment and application of SAR satellites. However, the automatic derivation of high-precision GCPs remains challenging, primarily due to two key factors: 1) the need for high-accuracy calibration of SAR geometric observations, and 2) the automatic detection and matching of candidate GCPs (CGCPs) across SAR images acquired from different look angles. This paper proposed an improved algorithm framework for GCP extraction, and demonstrated the application potential of China's SAR satellites. The main improvements are as follows: 1) Beam-dependent biases are incorporated into the geometric calibration process to achieve higher ranging accuracy. 2) A CGCP detector is designed that leverages shape characteristics to robustly identify CGCPs without relying on any external data. 3) An integrated matching scheme is proposed, which combines preprocessing, feature description, and transformation-search strategies to enable effective pairing of CGCPs. SAR imagery from China's Gaofen-3B (GF3B) and Superview Neo-2 (SVN2) 01/02 satellites is utilized, and geometric calibration is performed using the open-access corner reflector array in Australia. GCP extraction experiments are conducted in the Suzhou area, with 7,477 GCPs automatically extracted and a root mean square error (RMSE) better than 1 meter.