Sequential estimator for distributed scatterer interferometry

The launch of the wide-swath SAR missions with short repeat-pass cycles, such as Sentinel-1, will soon provide an unprecedented large InSAR data archive. Time-series analysis on the rapidly growing data will thus become computationally demanding for a systematic monitoring of earth surface deformation. As the state-of-the-art approach in differential InSAR time-series analysis, the distributed scatterer interferometric (DSI) techniques shall adapt agile processing schemes to deal with the emerging big data; an aspect to which limited attention has been dedicated. In this contribution, a sequential DSI scheme is proposed to address this demand. Based on SAR data reduction, the scheme allows for batch processing of the large data stacks while preserving the performance close to the Cramér-Rao Lower Bound. The performance of theof earth surface deformation. As the state-of-the-art approach in differential InSAR time-series analysis, the distributed scatterer interferometric (DSI) techniques shall adapt agile processing schemes to deal with the emerging big data; an aspect to which limited attention has been dedicated. In this contribution, a sequential DSI scheme is proposed to address this demand. Based on SAR data reduction, the scheme allows for batch processing of the large data stacks while preserving the performance close to the Cramér-Rao Lower Bound. The performance of the proposed sequential estimator is compared to the current DSI algorithms under two contradicting coherence scenarios. The application of the proposed sequential estimator to stacks proposed sequential estimator is compared to the current DSI algorithms under two contradicting coherence scenarios. The application of the proposed sequential estimator to stacks of Sentinel-1 data is ongoing.