Homa Ansari

Measuring 3-D Surface Motion With Future SAR Systems Based on Reflector Antennae

A conventional interferometric synthetic aperture radar (SAR) system provides 1-D line-of-sight motion measurements from repeat-pass observations. Two-dimensional motions may be measured by combining two observations from ascending and descending geometries. The third motion component may be retrieved by adding a third geometry and/or by integrating along-track measurements although with much reduced precision compared to the other two components. Several options exist to improve the accuracy of retrieving the third motion component, such as combining left- and right-looking observations or exploiting recently proposed innovative SAR acquisition modes (BiDiSAR and SuperSAR). These options are, however, challenging for future SAR systems based on large reflector antennae, due to lack of capability to electronic beam steering or frequent toggle between left- and right-looking modes. Therefore, in this letter, we assess and compare the realistic acquisition scenarios for a reflector-based SAR in an attempt to optimize the achievable 3-D precision. Investigating the squinted SAR geometry as one of the feasible scenarios, we show that a squint of 13.5° will yield comparable performance to the left-looking acquisition, while further squinting outperforms this or other feasible configurations. As an optimum configuration for 3-D retrieval, the squinted acquisition is further elaborated: the different acquisition plans considering a constellation of two satellites as well as the challenges for data processing are addressed.

Sequential Estimator: Toward Efficient InSAR Time Series Analysis

Wide-swath synthetic aperture radar (SAR) missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of interferometric SAR (InSAR) time series. However, the processing of the emerging Big Data is challenging for state-of-the-art InSAR analysis techniques. 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. Such interferograms are used to link the “older” data batches with the most recent acquisitions and thus to reconstruct the phase time series. This scheme avoids the necessity of reprocessing the entire data stack at the face of each new acquisition. The proposed estimator introduces negligible degradation compared to the Cramér–Rao lower bound under realistic coherence scenarios. The estimator may therefore be adapted for high-precision near-real-time processing of InSAR and accommodate the conversion of InSAR from an offline to a monitoring geodetic tool. The performance of the Sequential Estimator is compared to state-of-the-art techniques via simulations and application to Sentinel-1 data.

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.

Tandem-L performance analysis for three dimensional earth deformation monitoring

Interferometric synthetic aperture radar (InSAR) measurements are merely sensitive to the deformation along the Line of Sight (LOS) direction of the sensor. To improve the geometrical sensitivity and retrieve the three-dimensional deformation, the integration of InSAR from non-coplanar acquisitions as well as fusion with resolution-scale SAR image shift measurements has become a standard approach. Using different statistical measures, we assess and compare the influence of different image acquisition strategies as well as data fusion on the performance of InSAR in 3D deformation retrieval. Integrating nominal InSAR acquisitions, i.e. a set of measurements from ascending and descending tracks acquired from right-looking geometry, a strong correlation between the retrieved 3D parameters in the local vertical-north plane is observable. This correlation is sought to be decreased by non-nominal acquisitions; i.e. left-looking or squinted observations. These acquisition strategies are discussed for consideration in the future L-band mission Tandem-L.

Amplitude time series analysis in detection of persistent and temporal coherent scatterers

Constraining the deformation analysis to scatterers with high phase coherence, known as persistent scatterers (PS), in the InSAR time series plays a major role in advanced coherent approaches such as Persistent Scatterer Interferometry (PSI). Although crucial in overcoming the shortcomings of conventional InSAR in retrieving the deformation signals, this constraint appears to be too strict and lead to information loss in cases where the scatterers are coherent in merely a short period of the time series. Exploiting such scatterers, referred to as temporal coherent scatterers (TCS), is a necessity in PSI analysis in order to enhance the density of the resulted point clouds and consequently the quality of PSI. In here, statistical properties of the SAR calibrated amplitude time series are exploited to propose a new method for detection of the TCSs and estimation of the coherent intervals. The proposed methodology also allows for estimation of signal to clutter ratio (SCR) as an indication of the phase coherence. The method is evaluated using the simulated SAR stack as well as TerraSAR-X high resolution complex images.

Sequential estimator: A novel approach for efficient high-precision analysis of interferometric time series

Wide-swath satellite missions with short revisit times, such as Sentinel-1 and the planned NISAR and Tandem-L, provide an unprecedented wealth of interferometric time series and open 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 the 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 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.