Abstract
Modern Synthetic Aperture Radar (SAR) missions provide an unprecedented massive interferometric SAR (InSAR) time series. The processing of the Big InSAR Data is challenging for long-term monitoring. Indeed, as most deformation phenomena develop slowly, a strategy of a processing scheme can be worked on reduced volume data sets. This paper introduces a novel ComSAR algorithm based on a compression technique for reducing computational efforts while maintaining the performance robustly. The algorithm divides the massive data into many mini-stacks and then compresses them. The compressed estimator is close to the theoretical Cramer–Rao lower bound under a realistic C-band Sentinel-1 decorrelation scenario. Both persistent and distributed scatterers (PSDS) are exploited in the ComSAR algorithm. The ComSAR performance is validated via simulation and application to Sentinel-1 data to map land subsidence of the salt mine Vauvert area, France. The proposed ComSAR yields consistently better performance when compared with the state-of-the-art PSDS technique. We make our PSDS and ComSAR algorithms as an open-source TomoSAR package. To make it more practical, we exploit other open-source projects so that people can apply our PSDS and ComSAR methods for an end-to-end processing chain. To our knowledge, TomoSAR is the first public domain tool available to jointly handle PS and DS targets.
Highlights
Interferometry in the Big Data Era.Modern Synthetic Aperture Radar (SAR) satellite missions produce massive data with unprecedented characteristics [1,2,3]
The proposed ComSAR yields consistently better performance when compared with the state-of-the-art persistent and distributed scatterers (PSDS) technique
We show that the use of ComSAR to deal with SAR Sentinel-1 data offers two main advantages: (1) ComSAR can be adopted to work on massive time-series data for long-term high-precision monitoring; and (2) it yields consistently better performance when compared with the state-of-the-art PSDS interferometric SAR (InSAR) and PS interferometry (PSI) techniques
Summary
Interferometry in the Big Data Era. Modern Synthetic Aperture Radar (SAR) satellite missions produce massive data with unprecedented characteristics [1,2,3]. Modern Synthetic Aperture Radar (SAR) satellite missions produce massive data with unprecedented characteristics [1,2,3] They feature large coverages, short repeat–pass times, and high spatial resolution, opening a Big Data era for SAR data. Since the 2000s, InSAR time series techniques have been powerful techniques for estimating deformation from space. The principle of the InSAR time series is to take advantage of the redundancy information to minimize signal decorrelations to extract deformed signals robustly ([5,7]). While many time series InSAR methods have been developed in the last
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