Compressed SAR Interferometry in the Big Data Era

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.

Enhancing Coherence Images for Coherent Change Detection: An Example on Vehicle Tracks in Airborne SAR Images

In Synthetic Aperture Radar (SAR) interferometry, one of the most widely used measures for the quality of the interferometric phase is coherence. However, in favorable conditions coherence can also be used to detect subtle changes on the ground, which are not visible in the amplitude images. For such applications, i.e., coherent change detection, it is important to have a good contrast between the unchanged (high-coherence) parts of the scene and the changed (low-coherence) parts. In this paper, an algorithm is introduced that aims at enhancing this contrast. The enhancement is achieved by a combination of careful filtering of the amplitude images and the interferometric phase image. The algorithm is applied to an airborne interferometric SAR image pair recorded by the SmartRadar experimental sensor of Hensoldt Sensors GmbH. The data were recorded during a measurement campaign over the Bann B installations of POLYGONE Range in southern Rhineland-Palatinate (Germany), with a time gap of approximately four hours between the overflights. In-between the overflights, several vehicles were moved on the site and the goal of this work is to enhance the coherence image such that the tracks of these vehicles can be detected as completely as possible in an automated way. Several coherence estimation schemes found in the literature are explored for the enhancement, as well as several commonly used speckle filters. The results of these filtering steps are evaluated visually and quantitatively, showing that the mean gray-level difference between the low-coherence tracks and their high-coherence surroundings could be enhanced by at least 28%. Line extraction is then applied to the best enhancement. The results show that the tracks can be detected much more completely using the coherence contrast enhancement scheme proposed in this paper.

An Integrated Approach for Mapping Three-Dimensional CoSeismic Displacement Fields from Sentinel-1 TOPS Data Based on DInSAR, POT, MAI and BOI Techniques: Application to the 2021 Mw 7.4 Maduo Earthquake

Sentinel-1 Terrain Observation by Progressive Scans (TOPS) data have been widely applied in earthquake studies due to their open-source policy, short revisit cycle and wide coverage. However, significant near-fault displacement gradients and the moderate azimuth resolution of TOPS data make achieving high-precision along-track measurements challenging, which prevents the generation of high-quality three-dimensional (3D) displacement maps. Here, we propose an integrated method to retrieve high-quality 3D displacements based on the differential interferometric SAR (DInSAR), burst-overlap interferometry (BOI), multiple-aperture InSAR (MAI) and pixel offset tracking (POT) techniques, which are achieved to use only two track Sentinel-1 TOPS data with different viewing geometries. The key step of this method is using a weighted fusion algorithm with the interpolated BOI-derived and MAI-derived 3D displacements. In a case study of the 2021 Maduo earthquake, the calculated root mean square errors (RMSEs) from global navigation satellite system (GNSS) data and the InSAR-derived 3D displacement fields were found to be 6.3, 5.8 and 1.7 cm in north–south, east–west and up–down components, respectively. Moreover, the slip model of the 2021 Maduo earthquake jointly estimated by DInSAR and BOI measurements indicates that this seismic event was dominated by sinistral strike-slip motion mixed with some dip-slip movements; the estimated seismic moment was 1.75 × 1020 Nm, corresponding to a Mw 7.44 event.

High Performance Computing in Satellite SAR Interferometry: A Critical Perspective

Synthetic aperture radar (SAR) interferometry has rapidly evolved in the last decade and can be considered today as a mature technology, which incorporates computationally intensive and data-intensive tasks. In this paper, a perspective on the state-of-the-art of high performance computing (HPC) methodologies applied to spaceborne SAR interferometry (InSAR) is presented, and the different parallel algorithms for interferometric processing of SAR data are critically discussed at different levels. Emphasis is placed on the key processing steps, which typically occur in the interferometric techniques, categorized according to their computational relevance. Existing implementations of the different InSAR stages using diverse parallel strategies and architectures are examined and their performance discussed. Furthermore, some InSAR computational schemes selected in the literature are analyzed at the level of the entire processing chain, thus emphasizing their potentialities and limitations. Therefore, the survey focuses on the inherent computational approaches enabling large-scale interferometric SAR processing, thus offering insight into some open issues, and outlining future trends in the field.