Using ground control points (GCPs) can improve positioning accuracy in the calibration process, but the acquisition of GCPs is always difficult and high cost, especially in mountain areas and abroad. Thus, we proposed an improved calibration algorithm assisted by the digital elevation model data to calibrate geometric parameters for the single-scene synthetic aperture radar (SAR) image with no need for GCPs by providing an alternative solution that introduces topographic slope as an index of applicability and reliability. The algorithm includes three main steps: SAR image simulation, registration parameters calculation between the real and simulated images using a very large search window, and time delay estimation in the range and azimuth directions based on the above registration results. Experiments on Gaofen-3 SAR images of different modes show that the proposed approach provides good performance without any GCPs, reducing the errors of checkpoints from 151.3 to 26.6 m. Besides, the orthoimages further demonstrate the effectiveness of the algorithm in the whole image, especially in mountainous areas where the layover is well compensated. Finally, the applicability of the proposed method is analyzed, proving its great suitability for SAR images with terrain slopes greater than 0.05.
Polarization calibration (PolCal) is necessary for the quantitative application of polarimetric SAR data. The classic PolCal methods rely on corner reflectors (CRs) to calculate parameters, but the deployment of CRs is extremely expensive and they cannot even be deployed in complex terrains. Therefore, currently advanced methods can achieve calibration without relying on CRs. This method is based on the Bragg-like targets and uses the unitary zero helix (UZH) constraint to estimate the co-pol channel imbalance k for the segmented image by Gaussian Newton method. In this process, it is necessary to select appropriate range of k samples for fitting to obtain the final estimate of k. A fixed threshold may lead to improper sample selection, thereby reducing the accuracy of calibration. Therefore, this paper proposes an adaptive PolCal method, which introduces a coefficient of variation to adaptively select samples, and then ultimately estimates k using the best fit line. This paper conducts PolCal experiments using the image of GF3 02 satellite in the calibration field of Etuoke Banner, Inner Mongolia on January 1, 2022. The experiments demonstrate that the proposed method can enhance the stability of the PolCal method independent of CRs and further improve calibration accuracy.
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