Analysis of a Maximum Likelihood Phase Estimation Method for Airborne Multibaseline SAR Interferometry

Abstract

It has been shown using simulated data that phase estimation of cross-track multibaseline synthetic aperture radar (SAR) interferometric data was most efficiently achieved through a maximum likelihood (ML) method. In this paper, we apply and assess the ML approach on real data, acquired with an experimental Ka-band multibaseline system. Compared to simulated data, dealing with real data implies that several calibration steps be carried out to ensure that the data fit the model. A processing chain was, therefore, designed, including steps responsible for compensating for imperfections observed in the data, such as beam elevation angle dependent phase errors or phase errors caused by imperfect motion compensation. The performance of the ML phase estimation was evaluated by comparing it to results based on a coarse-to-fine (C2F) algorithm, where information from the shorter baselines was used only to unwrap the phase from the longest available baseline. For this purpose, flat areas with high coherence and homogeneous texture were selected in the acquired data. The results show that with only four looks, the noise level was marginally better with the C2F approach and contained fewer outliers. However, with more looks, the ML method consistently delivered better results: noise variance with the C2F approach was slightly but steadily larger than the variance obtained with ML method.