Accuracy Assessment for Multibaseline Phase Unwrapping Without Using External Reference Data

Abstract

Accuracy assessment of interferometric synthetic aperture radar (InSAR) products without external reference data (ERD) has been a long-standing challenge because traditional phase unwrapping (PU) is an ill-posed problem. The limitations in accuracy assessment make it impossible to verify the precision of the PU results under actual observation conditions, so that the reliability of InSAR products in practical applications is unknowable. However, multibaseline (MB) PU is well-posed, so its accuracy can be evaluated in a statistical sense without using ERD, i.e., even if there are no in situ data, the accuracy of the products generated by MB InSAR can still be assessed theoretically. In this article, by obtaining the closed form optimality condition of the Chinese remainder theorem (CRT) optimization model, the new independent quantitative index for MB PU accuracy evaluation was mathematically established. Interestingly, we found that: 1) the optimality condition is a sufficient and necessary condition for the accuracy assessment of the MB PU and 2) it is affected by the normal baseline lengths of the MB InSAR system and the interferogram noise intensity. To practically apply this mathematical condition, a deep convolutional neural network (DCNN) was developed to refine MB InSAR product accuracy. The validity and effectiveness of the proposed approach have been systematically verified using simulated and acquired interferometric datasets.