Distributed Scatterer Interferometry Research Articles

A Hybrid Approach for High-Precision Phase Estimation in Distributed Scatterer Interferometry

A Hybrid Approach for High-Precision Phase Estimation in Distributed Scatterer Interferometry

Distributed Scatterer Interferometry for Fast Decorrelation Scenarios Based on Sparsity Regularization

Distributed Scatterer Interferometry for Fast Decorrelation Scenarios Based on Sparsity Regularization

Nonlocal Phase Linking for Distributed Scatterer Interferometry

Nonlocal Phase Linking for Distributed Scatterer Interferometry

AdpPL: An adaptive phase linking-based distributed scatterer interferometry with emphasis on interferometric pair selection optimization and adaptive regularization

The distributed scatterer (DS) interferometry (DSI) technology is a powerful geodetic tool for measuring the deformation over abundant land covers. As a state-of-the-art DSI technology, the SqueeSAR approach considered the statistical behavior of the decorrelated DS and proposed the phase triangulation algorithm (PTA) as the pioneering implementation of the phase linking (PL) theory to estimate the equivalent single-reference (ESR) phases from all interferometric combinations. Subsequently, many advanced estimators of the systematic phase series have been introduced. Interestingly, this paper has demonstrated that the utilization of weakly coherent interferometric pairs is not conducive to the theoretical accuracy improvement of the PL theory in case of the fast decorrelation scenario. Moreover, the reduction of the small baseline scales will worsen the positive definite degree of the time series coherence matrix, which makes the ill-posed solution problem more serious. Therefore, this paper proposes a novel adaptive PL (AdpPL) method integrating the following two innovations for mapping surface deformation: 1) in terms of the interferometric pair selection optimization (IPSO), this paper performs the homogeneity measure-based IPSO (HoMeIPSO) method to remove the lowly coherent candidate subset progressively and select the moderate interferometric pairs adaptively; 2) in terms of the robust parameter estimation, an adaptive regularization (AdpReg) approach suggests selecting an optimal damping factor according to the minimum fitting error of all interferogram observables. Experimental results demonstrate that the proposed method can restore the systematic phase series more clearly and detect the deformation area with spatially highly dynamic characteristics successfully.

The Effect of SAR resolution and land-cover type on the distributed scatterer density in times-series of InSAR data

ABSTRACT Distributed scatterer interferometry (DS-InSAR) is an important time-series technique of surface deformation monitoring. The accuracy of the results highly depends on the density of DS, which is mainly influenced by synthetic aperture radar (SAR) image resolution and land cover. In this work, firstly, we use split spectrum technique to enable construction of SAR images with different bandwidth (i.e. different resolution). With the land-cover map as classification basis and mean coherence as DS selection index, we quantitatively analyse the behaviour of DS pixel density on different resolution SAR images and three land-cover types. We find that the DS density increases linearly with the increase of SAR system bandwidth, but the linear coefficient decreases with the increase of mean coherence. Secondly, we extend the coherence framework proposed by Zebker in 1992 to establish a model of relationship between mean coherence and bandwidth. The DS density by three land cover and different bandwidth SAR images was predicted by this model. When the mean coherence threshold is set to 0.4, the prediction model can agree with empirical results within 50% difference, and rather closer on impervious surface.

Distributed scatterer interferometry for forested and hilly areas using a topographical homogeneous filtering

ABSTRACT The application of interferometric synthetic aperture radar (InSAR) has been challenged over hilly and vegetated regions due to significant phase decorrelation. The spatiotemporal filtering techniques can partially overcome this, whereas the so-called ‘homogeneous pixels’ are selected based on the synthetic aperture radar (SAR) datasets and the computation is not satisfactorily efficient. In this letter, we propose the idea of interferogram homogeneous filtering relying on Light Detection and Ranging (LiDAR) derived topography features, instead of using amplitude or phase from the SAR images. The method exploits terrain slope and aspect to cluster topographically homogeneous pixels (THPs) and uses a coherence weighted phase-link (WPL) algorithm to reconstruct pixel phase histories. We compare the proposed method with two representative strategies, i.e., the amplitude statistically homogeneous pixels (SHPs) and the similar time-series interferometric phase pixels (STIPs). Results are given using L-band ALOS-1 data covering the forested Island of Lantau, Hong Kong. It is demonstrated that the LiDAR-derived THP has substantially decreased the computation load whereas the coherence of phase and the InSAR parameter estimation have been improved.

A New Likelihood Function for Consistent Phase Series Estimation in Distributed Scatterer Interferometry

The proper use of distributed scatterer (DS) can improve both the density and quality of synthetic aperture radar (SAR) interferometry (InSAR) measurements. A critical step in DS interferometry (DSI) is the restoration of a consistent phase series from SAR interferogram stacks. Most state-of-the-art algorithms adopt an approximate likelihood function to calculate the likelihood by replacing the true coherence matrix with its estimation, more specifically, the sample coherence matrix (SCM). However, this approximation has a drawback in that the coherence estimates are greatly biased when the coherence is low. In this study, we derive a new likelihood function without such an approximation. Accordingly, a DSI framework using this function for phase estimation and point selection is provided. In this framework, the new likelihood function serves as a cost function for phase estimation and a quality measure for DS selection. Its performance is investigated by experiments in a simulation study and a real-world case study using Sentinel-1 data over Shenzhen airport in China. The results reveal that the proposed DSI framework outperforms the existing state-of-the-art approaches in different scenarios, in terms of providing a more accurate estimation and improving DS density and coverage.

ARU-Net: Reduction of Atmospheric Phase Screen in SAR Interferometry Using Attention-Based Deep Residual U-Net

Atmospheric phase screen (APS) is a very critical issue for the application of interferometric synthetic aperture radar (InSAR) techniques. The spatial-temporal variations of APS are the dominant error source in interferograms and may completely mask displacement signals. Many external meteorological data-based methods and phase-based methods have been developed in the past decades, but all have their inherent limitations. In this article, we propose a deep learning-based method, which is based on an attention-based deep residual U-shaped network (ARU-Net), to mitigate atmospheric artifacts. With this approach, APS patches and clean interferogram patches are sampled from InSAR interferograms to train the network. After training, the network can be used to mitigate the APS for individual interferograms. Compared with the generic atmospheric correction model (GACOS) and the advanced time-series InSAR method distributed scatterer interferometry (DSI), the key advantage of our method is that atmospheric delay can be effectively learned and removed from individual high-resolution interferometric phase itself without external data. Accuracy was validated by using individual and stacked interferograms from TerraSAR-X data over the Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP) sites. The results showed that our method consistently delivered greater standard deviation (SD) reduction after APS correction than the GACOS method. Moreover, the time-series results were in agreement with the DSI and leveling measurements. The effectiveness of the proposed ARU-Net to remove APS effects from interferograms shows great potential for the development of a new set of deep learning-based APS reduction methods.

Potential of Using Phase Correlation in Distributed Scatterer InSAR Applied to Built Scenarios

The improved spatial resolution of Synthetic Aperture Radar (SAR) images from newly launched sensors has promoted a more frequent use of distributed scatterer (DS) interferometry (DSI) in urban monitoring, pursuing sufficient and detailed measurements. However, the commonly used statistical methods for homogeneous pixel clustering by exploring amplitude information are firstly, computationally intensive; furthermore, their necessity when applied to high-coherent built scenarios is little discussed in the literature. This paper explores the potential of using phase information for the detection of homogeneous pixels on built surfaces. We propose a simple phase-correlated pixel (PCP) clustering and introduce a coherence-weighted phase link (WPL), i.e., PCPWPL, to pursue a faster processing of interferogram phase denoising. Rather than relying on the statistical tests of amplitude characteristics, we exploit vector correlation in the complex domain to identify PCPs with similar phase observations, thus, avoiding the intensive hypothesis test. A coherence-weighted phase linking is applied for DS phase reconstruction. The estimation of geophysical parameters, e.g., deformation, is completed using an integrated network of persistent scatterers (PS) and DS. Efficiency of the proposed method is fairly illustrated by both synthetic and real data experiments. Pros and cons of the proposed PCPWPL were analyzed with the comparison to a conventional amplitude-based strategy using an X-band CosmoSkyMed dataset. It is demonstrated that the use of phase correlation is sufficient for DS monitoring in built scenarios, with equivalent measurement quantity and cheaper computational cost.

Integration of Time Series Sentinel-1 and Sentinel-2 Imagery for Crop Type Mapping over Oasis Agricultural Areas

Timely and accurate crop type mapping is a critical prerequisite for the estimation of water availability and environmental carrying capacity. This research proposed a method to integrate time series Sentinel-1 (S1) and Sentinel-2 (S2) data for crop type mapping over oasis agricultural areas through a case study in Northwest China. Previous studies using synthetic aperture radar (SAR) data alone often yield quite limited accuracy in crop type identification due to speckles. To improve the quality of SAR features, we adopted a statistically homogeneous pixel (SHP) distributed scatterer interferometry (DSI) algorithm, originally proposed in the interferometric SAR (InSAR) community for distributed scatters (DSs) extraction, to identify statistically homogeneous pixel subsets (SHPs). On the basis of this algorithm, the SAR backscatter intensity was de-speckled, and the bias of coherence was mitigated. In addition to backscatter intensity, several InSAR products were extracted for crop type classification, including the interferometric coherence, master versus slave intensity ratio, and amplitude dispersion derived from SAR data. To explore the role of red-edge wavelengths in oasis crop type discrimination, we derived 11 red-edge indices and three red-edge bands from Sentinel-2 images, together with the conventional optical features, to serve as input features for classification. To deal with the high dimension of combined SAR and optical features, an automated feature selection method, i.e., recursive feature increment, was developed to obtain the optimal combination of S1 and S2 features to achieve the highest mapping accuracy. Using a random forest classifier, a distribution map of five major crop types was produced with an overall accuracy of 83.22% and kappa coefficient of 0.77. The contribution of SAR and optical features were investigated. SAR intensity in VH polarization was proved to be most important for crop type identification among all the microwave and optical features employed in this study. Some of the InSAR products, i.e., the amplitude dispersion, master versus slave intensity ratio, and coherence, were found to be beneficial for oasis crop type mapping. It was proved the inclusion of red-edge wavelengths improved the overall accuracy (OA) of crop type mapping by 1.84% compared with only using conventional optical features. In comparison, it was demonstrated that the synergistic use of time series Sentinel-1 and Sentinel-2 data achieved the best performance in the oasis crop type discrimination.

Landslides detection through optimized hot spot analysis on persistent scatterers and distributed scatterers

Long-term InSAR techniques, such as Persistent Scatterer Interferometry and Distributed Scatterer Interferometry, are effective approaches able to detect slow-moving landslides with millimeter precision. This study presents a novel approach of optimized hot spot analysis (OHSA) on persistent scatterers (PS) and distributed scatterers (DS), and evaluates its performance on detection of landslides across the Volterra area in central Tuscany region of Italy. 1625 ascending and 2536 descending PS processed from eight years (2003–2010) of ENVISAT images were produced by the PS-InSAR technique. In addition, 16,493 ascending and 9746 descending PS/DS measurement points (MP) processed from four years (2011–2014 for ascending orbits and 2010–2013 for descending orbits) of COSMO-SkyMed images were collected by the SqueeSAR approach. The OHSA approach was then implemented on the derived PS and DS through the analysis of incremental spatial autocorrelation and the Getis-Ord Gi* statistics. As a result of OHSA, PS and DS MP that are statistically significant with velocity >|±2| mm/year, p-value < 0.01 and z-score >|±2.58| were recognized as hot spots (HS). Meanwhile, a landslide inventory covering the Volterra area was manually prepared as the reference data for accuracy assessment of landslide detection. The results indicate that, in terms of OHSA-derived ENVISAT HS, the detection accuracy can be improved from 23.3% to 25.3% and from 50.7% to 66.4%, with decreased redundancy from 5.3% to 3.7% and from 5.3% to 2.4%, for ascending and descending orbits, respectively. In addition, for OHSA-derived Cosmo-SkyMed HS, the detection accuracy can be improved from 57.7% to 70.3% and from 73.8% to 81.5%, with decreased redundancy from 3.1% to 1.7% and from 3.4% to 2.1%, for ascending and descending orbits, respectively. Compared to traditional HS analysis such as Persistent Scatterers Interferometry Hot Spot and Cluster Analysis (PSI-HCA), OHSA has the significant advantage that the scale distance used for the Getis-Ord Gi* statistics can be automatically determined by the analysis of incremental spatial autocorrelation and accordingly no manual intervention or additional digital terrain model (DTM) is further needed. The proposed method is very succinct and can be easily implemented in diverse geographic information system (GIS) platforms. To the best of our knowledge, this is the first time that OHSA has been applied to PS and DS.

Underground Coal Fires Identification and Monitoring Using Time-Series InSAR With Persistent and Distributed Scatterers: A Case Study of Miquan Coal Fire Zone in Xinjiang, China

The Xinjiang is an important coal production base in China and also a serious coal fire disaster area. Coal fires not only waste resources, but also cause air pollution and damage to the ecological environment. Hence, it is very important to identify and monitor the underground coal fire areas accurately and efficiently for the control of coal fires. Interferometric synthetic aperture radar (InSAR) technology identifies and monitors coal fire areas by monitoring surface subsidence caused by burned out area. Compared with traditional coal fire monitoring technology, InSAR technology has the advantages of all-weather and high efficiency. But the fire areas are often distributed in wild areas, this factor significantly limits the application of the traditional Persistent Scatterer interferometry (PSI) technology. In addition, Xinjiang coal fires are mostly located in historical goafs, so it is necessary to distinguish the subsidence caused by mining and coal fires. Therefore, distributed scatterer interferometry (DSI) technology is used to monitor the Miquan fire area in Xinjiang in this paper. The results show that compared with PSI technology, DSI technology can expand the number of effective monitoring points 124 times. On this basis, spatio-temporal analysis of surface subsidence in the study area suggests that the subsidence caused by mining and coal fires exhibits significantly different space-time evolution rules. Therefore, in the future, the coal fire area and mining area can be separated and identified according to these rules. The final identified coal fire area contains all measured coal fire points, and accurately monitors the fire extinguishing area.

Monitoring Coastal Reclamation Subsidence in Hong Kong with Distributed Scatterer Interferometry

Land subsidence has been a significant problem in land reclaimed from the sea, and it is usually characterized by a differential settlement pattern due to locally unconsolidated marine sediments and fill materials. Time series Synthetic Aperture Radar Interferometry (InSAR) techniques based on distributed scatterers (DS), which can identify sufficient measurement points (MPs) when point-wise radar targets are lacking, have great potential to measure such differential reclamation settlement. However, the computational time cost has been the main drawback of current distributed scatterer interferometry(DSI) for its applications compared to the standard PSI analysis. In this paper, we adopted an improved DSI processing strategy for a fast and robust analysis of land subsidence in reclaimed regions, which is characterized by an integration of fast statistically homogeneous pixel selection based (FaSHPS-based) DS detection and eigendecomposition phase optimization. We demonstrate the advantages of the proposed DSI strategy in computational efficiency and deformation estimation reliability by applying it to two TerraSAR-X image data stacks from 2008 to 2009 to retrieve land subsidence over two typical reclaimed regions of Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP). Compared with the state-of-the-art DSI methods, the proposed strategy significantly improves the computational efficiency, which is enhanced approximately 30 times in DS identification and 20 times in phase optimization. On average, the DSI strategy results in 7.8 and 3.7 times the detected number of MPs for HKIA and HKSP with respect to persistent scatter interferometry (PSI), which enables a very detailed characterization of locally differential settlement patterns. Moreover, the DSI-derived results agree well with the levelling survey measurements at HKIA, with a mean difference of 1.87 mm/yr and a standard deviation of 2.08 mm/yr. The results demonstrate that the proposed DSI strategy is effective at improving target density, accuracy and efficiency in monitoring ground deformation, particularly over reclaimed coastal areas.

A Review of Ten-Year Advances of Multi-Baseline SAR Interferometry Using TerraSAR-X Data

Since its launch in 2007, TerraSAR-X has continuously provided spaceborne synthetic aperture radar (SAR) images of our planet with unprecedented spatial resolution, geodetic, and geometric accuracy. This has brought life to the once inscrutable SAR images, which deterred many researchers. Thanks to merits like higher spatial resolution and more precise orbit control, we are now able to indicate individual buildings, even individual floors, to pinpoint targets within centimeter accuracy. As a result, multi-baseline SAR interferometric (InSAR) techniques are flourishing, from point target-based algorithms, to coherent stacking techniques, to absolute positioning of the former techniques. This article reviews the recent advances of multi-baseline InSAR techniques using TerraSAR-X images. Particular focus was put on our own development of persistent scatterer interferometry, SAR tomography, robust estimation in distributed scatterer interferometry and absolute positioning using geodetic InSAR. Furthermore, by introducing the applications associated with these techniques, such as 3D reconstruction and deformation monitoring, this article is also intended to give guidance to wider audiences who would like to resort to SAR data and related techniques for their applications.

The deformation analysis of Wenjiagou giant landslide by the distributed scatterer interferometry technique

After the deadly Ms 8.0 Wenchuan earthquake, the Wenjiagou landslide produced steep topography, a narrow gully and abundant loose sediments; these factors have contributed to the high debris flow risk in the Wenjiagou area during subsequent rainy seasons. At least five debris flows have occurred in the Wenjiagou area between September 24, 2008, and September 18, 2010, which resulted in seven casualties and an economic loss of approximately 446 million RMB. To reduce the risk of debris flows and landslides, the Wenjiagou Valley Debris Flow Control Project (WVDFCP), which cost over 2 billion RMB, was carried out and completed in 2011. The control measures of the project effectively reduced the scale and damage of the following debris flows. In this paper, the recent deformation of the giant landslide and its effect on the WVDFCP are evaluated by applying a time-series interferometric synthetic aperture radar (InSAR) technique based on distributed scatterers (DSs) to the Radardat-2 SAR data collected from June 2014 to September 2015. In addition, the experimental results show that most areas of the landslide are stable, with an average deformation rate of less than 5.0 mm/year. The results demonstrate that the control measures of the WVDFCP not only reduced the damage caused by the later debris flows but also contributed to the consolidation of the loose sediments in the Wenjiagou landslide area. The time-series InSAR technique based on the DSs of high-resolution SAR images is an important tool for deformation monitoring of earthquake-induced landslides.

Mathematical Framework for Phase-Triangulation Algorithms in Distributed-Scatterer Interferometry

To improve the spatial density of measurement points of persistent-scatterer interferometry, distributed scatterer (DS) should be considered and processed. An important procedure in DS interferometry is the phase triangulation (PT). This letter introduces two modified PT algorithms (i.e., equal-weighted PT and coherence-weighted PT) and analyzes the mathematical relations between different published PT methods (i.e., the maximum-likelihood phase estimator, least squares estimator, and eigendecomposition-based phase estimators). The analysis shows that the above five PT methods share very similar mathematical forms with different weight values in the estimation procedure.

Long term detection of water depth changes of coastal wetlands in the Yellow River Delta based on distributed scatterer interferometry

Coastal wetland ecosystems are among the most productive yet highly threatened systems in the world, and population growth and increasing economic development have resulted to extremely rapid degradation and loss of coastal wetlands. Spaceborne differential Interferometry SAR has proven a remarkable potential in wetland applications, including water level monitoring in high spatial resolution. However, due to the absence of ground observations for calibration and validation, long term monitoring of water depth, which is essential to evaluate ecosystem health of wetlands, is difficult to be estimated from spaceborne InSAR data. We present a new differential synthetic aperture radar method for temporal evolution of water depth in wetlands. The presented technique is based on distributed scatter interferogram technique in order to provide a spatially dense hydrological observation for coastal wetlands, which are characterized by high temporal decorrelation. This method adapts a strategy by forming optimum interferogram network to get a balance between maximum interferometric information preservation and computational cost reduction, and implements spatial adaptive filtering to reduce noise and enhance fringe visibility on distributed scatterers. Refined InSAR observation is tied to absolute reference frame to generate long term high resolution water level time-series using stage data. We transform water level time-series to long term observation of water depth with assistance of a dense measurement network of water depth. We present water depth time-series obtained using the data acquired from 2007 to 2010 by the ALOS satellite, which supplied significant information to evaluate ecological performance of wetland restoration in the Yellow River Delta.

A Distributed Scatterer Interferometry Approach for Precision Monitoring of Known Surface Deformation Phenomena

This paper presents a new technique for mapping mean deformation velocity in highly decorrelated areas with known deformation patterns, exploiting high-resolution synthetic aperture radar (SAR) data. The implemented method is based on distributed scatterers and first makes use of the Anderson-Darling (AD) statistical test to identify homogenous patches of pixels based on SAR amplitude images. Then, a robust object adaptive parameter estimation is performed to estimate the local gradients of deformation velocity and the local gradients of residual DEM in range and azimuth directions for these patches, utilizing small baseline differential interferograms. Finally, the information obtained from different patches is connected to get the deformation velocity, via a 2-D model-based deformation integration using Bayesian inference. Compared with published multitemporal interferometric work, the main advantage of the newly developed algorithm is that it does not require any phase unwrapping, and because of this, the method is largely insensitive to decorrelation phenomenon occurring in natural terrains and the availability of persistent scatterers (PSs), in contrast to the coherent stacking techniques such as PS interferometry, small baseline subset algorithm, and SqueeSAR. The method is computationally inexpensive with respect to SqueeSAR as only the small baseline interferograms are used for the processing. The method provides spatially dense deformation velocity maps at a suitable object resolution, as compared with a few measured points provided by the stacking techniques in difficult decorrelated regions. High Resolution Spotlight TerraSAR-X data set of Lueneburg in Germany is used as a processing example of this technique.

Multi-interferogram method for measuring interseismic deformation: Denali Fault, Alaska

SummaryStudies of interseismic strain accumulation are crucial to our understanding of continental deformation, the earthquake cycle and seismic hazard. By mapping small amounts of ground deformation over large spatial areas, InSAR has the potential to produce continental-scale maps of strain accumulation on active faults. However, most InSAR studies to date have focused on areas where the coherence is relatively good (e.g. California, Tibet and Turkey) and most analysis techniques (stacking, small baseline subset algorithm, permanent scatterers, etc.) only include information from pixels which are coherent throughout the time-span of the study. In some areas, such as Alaska, where the deformation rate is small and coherence very variable, it is necessary to include information from pixels which are coherent in some but not all interferograms. We use a three-stage iterative algorithm based on distributed scatterer interferometry. We validate our method using synthetic data created using realistic parameters from a test site on the Denali Fault, Alaska, and present a preliminary result of 10.5 ± 5.0 mm yr−1 for the slip rate on the Denali Fault based on a single track of radar data from ERS1/2.