Abstract
Abstract. The phase estimation of cross-track multibaseline synthetic aperture interferometric data is usually thought to be very efficiently achieved using the maximum likelihood (ML) method. The suitability of this method is investigated here as applied to airborne single pass multibaseline data. Experimental interferometric data acquired with a Ka-band sensor were processed using (a) a ML method that fuses the complex data from all receivers and (b) a coarse-to-fine method that only uses the intermediate baselines to unwrap the phase values from the longest baseline. The phase noise was analyzed for both methods: in most cases, a small improvement was found when the ML method was used.
Highlights
Multibaseline cross-track SAR interferometry is an extension of InSAR, whereby multiple baselines combine the advantages of shorter and longer baselines: simple phase unwrapping of interferograms from short baselines and lower sensitivity to phase noise from longer baselines (Rosen, 2000)
maximum likelihood (ML) phase estimation was demonstrated to be appropriate for InSAR processing of single pass high resolution multibaseline airborne data
The results were found to be very close between the C2F and ML methods
Summary
Multibaseline cross-track SAR interferometry is an extension of InSAR, whereby multiple baselines combine the advantages of shorter and longer baselines: simple phase unwrapping of interferograms from short baselines and lower sensitivity to phase noise from longer baselines (Rosen, 2000). The coarse-to-fine (C2F) phase unwrapping method (Magnard, 2014 and Essen, 2007) uses data from the shorter baselines to unwrap the interferogram based on the longest baseline. This method keeps the unwrapped phase information from the longest baseline, discarding information from the other baselines. The maximum likelihood (ML) method calculates a most-likely phase from arrays of focused SAR data (Single Look Complex data) according to a model (Lombardo, 1997). This allows use of all the data and should improve the noise level and reliability. Several other methods such as least squares or weighted least squares can be used to calculate the unwrapped phase; they were compared in (Lombardini, 2001), showing their advantages and shortcomings
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