Efficient InSAR Time Series Analysis in the Era of Big Data

Abstract

Wide-swath satellite missions with short revisit times, such as Sentinel-1, provide an unprecedented wealth of interferometric time series and opens new opportunities for systematic monitoring of the Earth surface. The processing of the emerging Big Data with the state-of-the-art InSAR time series analysis techniques is, however, challenging. This contribution introduces a novel approach, named Sequential Estimator, for efficient estimation of the interferometric phase from long InSAR time series. The algorithm uses recursive estimation and analysis of the data covariance matrix via division of the data into small batches, followed by the compression of the data batches. From each compressed data batch artificial interferograms are formed, resulting in a strong data reduction. This scheme avoids the necessity of re-processing the entire data stack at the face of each new acquisition. It is shown that the proposed estimator introduces negligible degradation compared to the Cramér-Rao-Lower-Bound. The estimator may therefore be adapted for high-precision Near-Real-Time processing of InSAR and accommodate the conversion of InSAR from an off-line to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to the state-of-the-art techniques via simulations and application to Sentinel-1 data.