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7 pages, 731 KiB

Open AccessEditorial

Advances in InSAR Imaging and Data Processing

by Lei Zhang and Zhong Lu

Remote Sens. 2022, 14(17), 4307; https://doi.org/10.3390/rs14174307 - 1 Sep 2022

Cited by 9 | Viewed by 3513

Abstract

Through different phases of synthetic aperture radar (SAR) data acquired on different dates and/or at different satellite imaging locations, the interferometric SAR (InSAR) technique has long been used to map ground deformation or generate global digital elevation model (DEM) (e [...] Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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Research

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21 pages, 6170 KiB

Open AccessArticle

Mapping the Recent Vertical Crustal Deformation of the Weihe Basin (China) Using Sentinel-1 and ALOS-2 ScanSAR Imagery

by Feifei Qu, Qin Zhang, Yufen Niu, Zhong Lu, Shuai Wang, Chaoying Zhao, Wu Zhu, Wei Qu and Chengsheng Yang

Remote Sens. 2022, 14(13), 3182; https://doi.org/10.3390/rs14133182 - 3 Jul 2022

Cited by 9 | Viewed by 2379

Abstract

The Weihe Basin, located in central China, is a Cenozoic rift basin that is characterized by a complicated geological background, with intense tectonic movement and relatively significant seismic activity. The faulting behaviors, slip rates, geometrical structures, and possible surface traces of the active [...] Read more.

The Weihe Basin, located in central China, is a Cenozoic rift basin that is characterized by a complicated geological background, with intense tectonic movement and relatively significant seismic activity. The faulting behaviors, slip rates, geometrical structures, and possible surface traces of the active faults in the Weihe Basin are still not well known. The goal of this work is to provide a more complete picture of recent vertical ground deformation of the basin and to identify active faults. We derived two basin-wide average InSAR deformation maps during 2015–2019 using C-band Sentinel-1A/B and L-band ALOS PALSAR2 ScanSAR imagery. The basin-wide vertical and east–west deformation components decomposed from ascending and descending InSAR measurements show that vertical movement dominates the deformation of the Weihe Basin. Subsidence and uplift maxima both occurred over the Xi’an region at rates of about −146 and 20 mm/y, respectively. A subsidence of ~38 mm/y was found at Sanyuan, Fuping, Weinan, Lantian, Qianxian, and Xingping while an uplift of ~11 mm/y was found at Fufeng, Huxian, Jingyang, Fuping, Pucheng, and Huaxian. The significant vertical deformation in these regions is spatially linked to previously identified or unmapped faults. A slip rate of ~13.7 mm/y on faults F20, F6, F10, and F7 explained the observed uplift of up to 5 mm/y in the Fufeng and Wugong areas. Furthermore, surface fault traces were clearly identified for faults F7-1, F8-1, F20, F25, and F26 based on discontinuities in the InSAR-derived vertical deformation measurements. Our results provide an accurate and economical way to delineate the surface deformation and fault movement and the associated geohazards over the Weihe Basin, and offer independent unprecedented data for further geological and geophysical interpretation. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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22 pages, 5860 KiB

Open AccessArticle

BM3D Denoising for a Cluster-Analysis-Based Multibaseline InSAR Phase-Unwrapping Method

by Zhihui Yuan, Tianjiao Chen, Xuemin Xing, Wei Peng and Lifu Chen

Remote Sens. 2022, 14(8), 1836; https://doi.org/10.3390/rs14081836 - 11 Apr 2022

Cited by 11 | Viewed by 3113

Abstract

Multibaseline (MB) phase unwrapping (PU) is a key processing technique in MB interferometric synthetic aperture radar (InSAR). As one of the most popular methods, the cluster analysis (CA)-based MBPU method often suffers from the problem of low noise robustness. Therefore, the block-matching and [...] Read more.

Multibaseline (MB) phase unwrapping (PU) is a key processing technique in MB interferometric synthetic aperture radar (InSAR). As one of the most popular methods, the cluster analysis (CA)-based MBPU method often suffers from the problem of low noise robustness. Therefore, the block-matching and 3D filtering (BM3D) algorithm, one of the most effective filtering methods for image denoising, is applied to improve the performance of the method. Five different filtering strategies for applying BM3D are proposed in the paper: interferogram filtering (IFF), intercept filtering (ICF), cluster number filtering (CNF), unwrapped phase filtering (UPF), and simultaneous filtering (STF). In particular, while keeping the general structure of BM3D, four different similarity measures are defined for interferograms, intercepts, clusters, and unwrapped phases to accommodate the special characteristics of different filtering objects. Experiments on synthesized and real InSAR datasets prove their feasibility and effectiveness, and the experiment results show that (1) the PU accuracy and robustness of the CA-based MBPU method can be greatly improved by adding BM3D denoising; (2) simultaneous filtering of interferograms, intercepts, cluster numbers, and unwrapped phases works best, but with the worst time complexity; (3) when filtering is performed for only one object of the CA-based MBPU method, the filtering effect of CNF and UPF is better than that of IFF and ICF; and (4), considering the three indicators of PUSR, NRSE, and time consumption, CNF and UPF should be the best choices. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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23 pages, 8145 KiB

Open AccessArticle

Adaptive Square-Root Unscented Kalman Filter Phase Unwrapping with Modified Phase Gradient Estimation

by Yansuo Zhang, Shubi Zhang, Yandong Gao, Shijin Li, Yikun Jia and Minggeng Li

Remote Sens. 2022, 14(5), 1229; https://doi.org/10.3390/rs14051229 - 2 Mar 2022

Cited by 12 | Viewed by 2643

Abstract

Phase unwrapping (PU) is a key program in data processing in the interferometric synthetic aperture radar (InSAR) technique, and its accuracy directly affects the quality of final SAR data products. However, PU in regions with large gradient changes and high noise has always [...] Read more.

Phase unwrapping (PU) is a key program in data processing in the interferometric synthetic aperture radar (InSAR) technique, and its accuracy directly affects the quality of final SAR data products. However, PU in regions with large gradient changes and high noise has always been a difficult problem. To overcome the limitation, this article proposes an adaptive square-root unscented Kalman filter PU method. Specifically, a modified phase gradient estimation (PGE) algorithm is proposed, in which a Butterworth low-pass filter is embedded, and the PGE window can be adaptively adjusted according to phase root-mean-square errors of pixels. Furthermore, the outliers of the PGE results are detected and revised to obtain high-precision vertical and horizontal phase gradients. Finally, the unwrapped phase is calculated by the adaptive square-root unscented Kalman filter method. To the best of our knowledge, this article is the first to combine the modified PGE with an adaptive square-root unscented Kalman filter for PU. Two sets of simulated data and a set of TerraSAR-X/TanDEM-X real data were used for experimental verification. The experimental results demonstrated that the various improvement measures proposed in this article were effective. Additionally, compared with the minimum-cost flow algorithm (MCF), statistical-cost network-flow algorithm (SNAPHU) and unscented Kalman filter PU (UKFPU), the proposed method had better accuracy and model robustness. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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19 pages, 24762 KiB

Open AccessArticle

Ground Deformation Pattern Analysis and Evolution Prediction of Shanghai Pudong International Airport Based on PSI Long Time Series Observations

by Xin Bao, Rui Zhang, Age Shama, Song Li, Lingxiao Xie, Jichao Lv, Yin Fu, Renzhe Wu and Guoxiang Liu

Remote Sens. 2022, 14(3), 610; https://doi.org/10.3390/rs14030610 - 27 Jan 2022

Cited by 23 | Viewed by 3905

Abstract

Being built on the reclamation area, Shanghai Pudong International Airport (SPIA) has been undergoing uneven subsidence since the beginning of its operation in 1999. In order to explore the evolution characteristics of ground deformation in the SPIA reclamation area and further provide assurance [...] Read more.

Being built on the reclamation area, Shanghai Pudong International Airport (SPIA) has been undergoing uneven subsidence since the beginning of its operation in 1999. In order to explore the evolution characteristics of ground deformation in the SPIA reclamation area and further provide assurance for the airport’s safe operation, 141 Sentinel-1A images from October 2016 to September 2021 were selected to acquire time-series ground deformation observations by the StaMPS PSI processing procedure. We subsequently built a ground deformation prediction model using the Long Short Term Memory (LSTM) neural network for the short-term prediction of the SPIA deformation severity area. On this basis, the spatial-temporal evolution trends of SPIA ground deformation in the reclamation area were revealed concerning the influence and mode of action of geological conditions and environmental factors. Finally, we proposed targeted recommendations and strategies for the comprehensive ground deformation prevention and control needs of SPIA. The results indicated that the SPIA exhibits overall subsidence in the eastern part, with the maximum deformation rate reaching −57.29 mm/a. Meanwhile, the central and western part has a local uplift with the maximum deformation rate reaching 32.76 mm/a. The proposed LSTM ground deformation prediction model demonstrated excellent robustness in the region of uneven deformation, and the prediction results were in high agreement with the StaMPS PSI monitoring results. The time-series observations and prediction results are expected to provide references for the expansion project of SPIA and help the research of ground deformation and prevention in related fields. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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19 pages, 114272 KiB

Open AccessArticle

Improving CPT-InSAR Algorithm with Adaptive Coherent Distributed Pixels Selection

by Longkai Dong, Chao Wang, Yixian Tang, Hong Zhang and Lu Xu

Remote Sens. 2021, 13(23), 4784; https://doi.org/10.3390/rs13234784 - 25 Nov 2021

Cited by 2 | Viewed by 2408

Abstract

The Coherent Pixels Technique Interferometry Synthetic Aperture Radar (CPT-InSAR) method of inverting surface deformation parameters by using high-quality measuring points possesses the flaw inducing sparse measuring points in non-urban areas. In this paper, we propose the Adaptive Coherent Distributed Pixel InSAR (ACDP-InSAR) method, [...] Read more.

The Coherent Pixels Technique Interferometry Synthetic Aperture Radar (CPT-InSAR) method of inverting surface deformation parameters by using high-quality measuring points possesses the flaw inducing sparse measuring points in non-urban areas. In this paper, we propose the Adaptive Coherent Distributed Pixel InSAR (ACDP-InSAR) method, which is an adaptive method used to extract Distributed Scattering Pixel (DSP) based on statistically homogeneous pixel (SHP) cluster tests and improves the phase quality of DSP through phase optimization, which cooperates with Coherent Pixel (CP) for the retrieval of ground surface deformation parameters. For a region with sparse CPs, DSPs and its SHPs are detected by double-layer windows in two steps, i.e., multilook windows and spatial filtering windows, respectively. After counting the pixel number of maximum SHP cluster (MSHPC) in the multilook window based on the Anderson–Darling (AD) test and filtering out unsuitable pixels, the candidate DSPs are selected. For the filtering window, the SHPs of MSHPC’ pixels within the new window, which is different compared with multilook windows, were detected, and the SHPs of DSPs were obtained, which were used for coherent estimation. In phase-linking, the results of Eigen decomposition-based Maximum likelihood estimator of Interferometric phase (EMI) results are used as the initial values of the phase triangle algorithm (PTA) for the purpose of phase estimation (hereafter called as PTA-EMI). The DSPs and estimated phase are then combined with CPs in order to retrievesurface deformation parameters. The method was validated by two cases. The results show that the density of measuring points increased approximately 6–10 times compared with CPT-InSAR, and the quality of the interferometric phase significantly improved after phase optimization. It was demonstrated that the method is effective in increasing measuring point density and improving phase quality, which increases significantly the detectability of the low coherence region. Compared with the Distributed Scatterer InSAR (DS-InSAR) technique, ACDP-InSAR possesses faster processing speed at the cost of resolution loss, which is crucial for Earth surface movement monitoring at large spatial scales. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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26 pages, 19630 KiB

Open AccessArticle

Detecting Rock Glacier Displacement in the Central Himalayas Using Multi-Temporal InSAR

by Xuefei Zhang, Min Feng, Hong Zhang, Chao Wang, Yixian Tang, Jinhao Xu, Dezhao Yan and Chunling Wang

Remote Sens. 2021, 13(23), 4738; https://doi.org/10.3390/rs13234738 - 23 Nov 2021

Cited by 27 | Viewed by 4220

Abstract

Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly [...] Read more.

Rock glaciers represent typical periglacial landscapes and are distributed widely in alpine mountain environments. Rock glacier activity represents a critical indicator of water reserves state, permafrost distribution, and landslide disaster susceptibility. The dynamics of rock glacier activity in alpine periglacial environments are poorly quantified, especially in the central Himalayas. Multi-temporal Interferometric Synthetic Aperture Radar (MT-InSAR) has been shown to be a useful technique for rock glacier deformation detection. In this study, we developed a multi-baseline persistent scatterer (PS) and distributed scatterer (DS) combined MT-InSAR method to monitor the activity of rock glaciers in the central Himalayas. In periglacial landforms, the application of the PS interferometry (PSI) method is restricted by insufficient PS due to large temporal baseline intervals and temporal decorrelation, which hinder comprehensive measurements of rock glaciers. Thus, we first evaluated the rock glacier interferometric coherence of all possible interferometric combinations and determined a multi-baseline network based on rock glacier coherence; then, we constructed a Delaunay triangulation network (DTN) by exploiting both PS and DS points. To improve the robustness of deformation parameters estimation in the DTN, we combined the Nelder–Mead algorithm with the M-estimator method to estimate the deformation rate variation at the arcs of the DTN and introduced a ridge-estimator-based weighted least square (WLR) method for the inversion of the deformation rate from the deformation rate variation. We applied our method to Sentinel-1A ascending and descending geometry data (May 2018 to January 2019) and obtained measurements of rock glacier deformation for 4327 rock glaciers over the central Himalayas, at least more than 15% detecting with single geometry data. The line-of-sight (LOS) deformation of rock glaciers in the central Himalayas ranged from −150 mm to 150 mm. We classified the active deformation area (ADA) of all individual rock glaciers with the threshold determined by the standard deviation of the deformation map. The results show that 49% of the detected rock glaciers (monitoring rate greater than 30%) are highly active, with an ADA ratio greater than 10%. After projecting the LOS deformation to the steep slope direction and classifying the rock glacier activity following the IPA Action Group guideline, 12% of the identified rock glaciers were classified as active and 86% were classified as transitional. This research is the first multi-baseline, PS, and DS network-based MT-InSAR method applied to detecting large-scale rock glaciers activity. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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11 pages, 3368 KiB

Open AccessCommunication

P-Band InSAR for Geohazard Detection over Forested Terrains: Preliminary Results

by Yuankun Xu, Zhong Lu and Jin-Woo Kim

Remote Sens. 2021, 13(22), 4575; https://doi.org/10.3390/rs13224575 - 14 Nov 2021

Cited by 7 | Viewed by 6644

Abstract

Decorrelation of X, C, and L-band InSAR (Interferometric Synthetic Aperture Radar) over densely vegetated regions is a common obstacle for detecting ground deformation beneath forest canopies. Using long-wavelength P-band SAR sensors (wavelength of 69.72 cm), which can penetrate through dense forests and collect [...] Read more.

Decorrelation of X, C, and L-band InSAR (Interferometric Synthetic Aperture Radar) over densely vegetated regions is a common obstacle for detecting ground deformation beneath forest canopies. Using long-wavelength P-band SAR sensors (wavelength of 69.72 cm), which can penetrate through dense forests and collect relatively consistent signals from ground surface, is one potential solution. Here, we experimented using the NASA JPL (Jet Propulsion Laboratory)’s P-band AirMOSS (Airborne Microwave Observatory of Subcanopy and Subsurface) radar system to collect repeat-pass P-band SAR data over densely vegetated regions in Oregon and California (USA), and generated by far the first P-band InSAR results to test the capability of P-band InSAR for geohazard detection over forested terrains. Our results show that the AirMOSS P-band InSAR could retain coherence two times as high as the L-band satellite ALOS-2 (Advanced Land Observing Satellite-2) data, and was significantly more effective in discovering localized geohazards that were unseen by the ALOS-2 interferograms over densely vegetated areas. Our results suggest that the airborne P-band InSAR could be a revolutionary tool for studying geohazards under dense forest canopies. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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20 pages, 10829 KiB

Open AccessArticle

An Improved Phase Unwrapping Method Based on Hierarchical Networking and Constrained Adjustment

by Wenxiang Mao, Sai Wang, Bing Xu, Zhiwei Li and Yan Zhu

Remote Sens. 2021, 13(21), 4193; https://doi.org/10.3390/rs13214193 - 20 Oct 2021

Cited by 7 | Viewed by 3018

Abstract

Accurate phase unwrapping (PU) is a precondition and key for using synthetic aperture radar interferometry (InSAR) technology to successfully invert topography and monitor surface deformations. However, most interferograms are seriously polluted by noise in the low-quality regions, which poses difficulties for PU. Therefore, [...] Read more.

Accurate phase unwrapping (PU) is a precondition and key for using synthetic aperture radar interferometry (InSAR) technology to successfully invert topography and monitor surface deformations. However, most interferograms are seriously polluted by noise in the low-quality regions, which poses difficulties for PU. Therefore, using the strategy of leveling network adjustment, this paper proposes an improved PU method based on hierarchical networking and constrained adjustment. This method not only limits the phase error transfer of low-quality points, but also takes the PU results of high-quality points as control points and uses the network adjustment method with constraints to unwrap low-quality points, which effectively inhibits the influence of noise and improves the accuracy of unwrapping. Regardless of the unwrapping method used for high-quality points, the unwrapping accuracy of low-quality points can always be improved. Compared with other traditional two-dimensional phase unwrapping workflows, this method can more accurately recover the phase of low-coherence regions only through the interferogram. A simulation experiment showed that the local noise of the interferogram was effectively inhibited, and the PU accuracy of the low-quality regions was improved by 16–46% compared with different traditional methods. For a real-data experiment of mining area with low coherence, the PU result of our proposed method had fewer residues and lower phase standard deviation than traditional methods, further indicating the practicability and robustness of the proposed method. The work in this paper has considerable practical significance for recovering the decoherence phase with serious local noise such as mining centers and groundwater subsidence centers. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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20 pages, 8618 KiB

Open AccessArticle

An Adaptive Weighted Phase Optimization Algorithm Based on the Sigmoid Model for Distributed Scatterers

by Shijin Li, Shubi Zhang, Tao Li, Yandong Gao, Xiaoqing Zhou, Qianfu Chen, Xiang Zhang and Chao Yang

Remote Sens. 2021, 13(16), 3253; https://doi.org/10.3390/rs13163253 - 17 Aug 2021

Cited by 10 | Viewed by 2050

Abstract

Distributed scatterers (DSs) have been widely used in the time series interferometric synthetic aperture radar technique, which compensates for the insufficient density of persistent scatterers (PSs) in nonurban areas. In contrast to PS, DS is vulnerable to temporal and geometric decorrelation effects. Thus, [...] Read more.

Distributed scatterers (DSs) have been widely used in the time series interferometric synthetic aperture radar technique, which compensates for the insufficient density of persistent scatterers (PSs) in nonurban areas. In contrast to PS, DS is vulnerable to temporal and geometric decorrelation effects. Thus, phase optimization processing for DS is essential for reliable deformation parameter estimation. Advanced research has revealed that the application of all possible interferometric pairs will be more conducive to the reduction in phase biases. However, the low-coherence pixels will inevitably increase the difficulty of phase optimization and introduce unpredictable negative effects, which will reduce the effect of phase optimization. Therefore, this study proposed an advanced adaptive weighted phase optimization algorithm (AWPOA). In the AWPOA, the adaptive weighting strategy based on the sigmoid model was first proposed to assign more reasonable weights to pixels of different quality, which can efficiently reduce the negative influence of low-coherence pixels and improve the optimization performance. Moreover, coherence bias correction based on the second-kind statistics and an efficient solution strategy based on eigenvalue decomposition were derived and applied to achieve optimal phase series retrieval. The experimental results validated against both simulated and two sets of TerraSAR-X data demonstrated the overall superiority of the AWPOA over traditional phase optimization algorithms (POAs). Specifically, the processing efficiency of the eigenvalue decomposition solution strategy used in AWPOA was nearly 20 times faster than that of the PTA iterative solution strategy under the case without bias correction. Although bias correction increased the processing time, the optimization effect was significantly improved. Moreover, in terms of the quantitative evaluation indexes with the residual and the sum of the phase difference, the mean value of the improvement percentage of the AWPOA was increased by more than 12%, and the standard deviation was reduced by more than 1% over the traditional POAs, indicating its superior optimization performance and noise robustness. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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20 pages, 6371 KiB

Open AccessArticle

Underground Goaf Parameters Estimation by Cross-Iteration with InSAR Measurements

by Weihao Zhang, Jiancun Shi, Huiwei Yi, Yan Zhu and Bing Xu

Remote Sens. 2021, 13(16), 3204; https://doi.org/10.3390/rs13163204 - 12 Aug 2021

Cited by 7 | Viewed by 2073

Abstract

Determining the geographic location and spatial distribution of underground goaf is of great significance for the prevention of mining subsidence hazards and the detection of illegal mining. However, traditional goaf detection techniques mainly focus on geophysical methods that are labor intensive, have low [...] Read more.

Determining the geographic location and spatial distribution of underground goaf is of great significance for the prevention of mining subsidence hazards and the detection of illegal mining. However, traditional goaf detection techniques mainly focus on geophysical methods that are labor intensive, have low efficiency, and are expensive. Due to the large range and off-site monitoring capability of interferometric synthetic aperture radar (InSAR) techniques, research on goaf location detection based on InSAR measurements has been increasing. This paper proposes a new method for locating underground goaf based on cross-iteration and InSAR measurements. Firstly, the functional relationship between the geometric parameters of the goaf and the line of sight (LOS) deformation retrieved by InSAR techniques is constructed. Then, the three initial model parameters of the probability integration method (PIM) are determined by mining geological conditions. Finally, the cross-iteration method is used to determine the parameters to characterize the spatial location of underground goaf. The experimental results show that the average relative errors of the simulated experiment and the real experiment are 1.5% and 5.1%, respectively, and the inverted goaf parameters are in good agreement with the real values. Moreover, the proposed method only requires the main lithology of the overlying rock in the goaf and does not depend on the accuracy of PIM model parameters. Therefore, this method has engineering application value for the detection of goaf lacking actual measurement data or that caused by illegal mining. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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22 pages, 22593 KiB

Open AccessArticle

Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

by Mingjie Liao, Rui Zhang, Jichao Lv, Bin Yu, Jiatai Pang, Ran Li, Wei Xiang and Wei Tao

Remote Sens. 2021, 13(15), 3044; https://doi.org/10.3390/rs13153044 - 3 Aug 2021

Cited by 15 | Viewed by 2707

Abstract

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain [...] Read more.

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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16 pages, 12150 KiB

Open AccessArticle

Calibration Method of Array Errors for Wideband MIMO Imaging Radar Based on Multiple Prominent Targets

by Zheng Zhao, Weiming Tian, Yunkai Deng, Cheng Hu and Tao Zeng

Remote Sens. 2021, 13(15), 2997; https://doi.org/10.3390/rs13152997 - 30 Jul 2021

Cited by 8 | Viewed by 2454

Abstract

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration [...] Read more.

Wideband multiple-input-multiple-output (MIMO) imaging radar can achieve high-resolution imaging with a specific multi-antenna structure. However, its imaging performance is severely affected by the array errors, including the inter-channel errors and the position errors of all the transmitting and receiving elements (TEs/REs). Conventional calibration methods are suitable for the narrow-band signal model, and cannot separate the element position errors from the array errors. This paper proposes a method for estimating and compensating the array errors of wideband MIMO imaging radar based on multiple prominent targets. Firstly, a high-precision target position estimation method is proposed to acquire the prominent targets’ positions without other equipment. Secondly, the inter-channel amplitude and delay errors are estimated by solving an equation-constrained least square problem. After this, the element position errors are estimated with the genetic algorithm to eliminate the spatial-variant error phase. Finally, the feasibility and correctness of this method are validated with both simulated and experimental datasets. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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18 pages, 4398 KiB

Open AccessArticle

Novel Model-Based Approaches for Non-Homogenous Atmospheric Compensation of GB-InSAR in the Azimuth and Horizontal Directions

by Jie Liu, Honglei Yang, Linlin Xu and Tao Li

Remote Sens. 2021, 13(11), 2153; https://doi.org/10.3390/rs13112153 - 31 May 2021

Cited by 6 | Viewed by 2229

Abstract

Atmospheric disturbance is a main interference for deformation monitoring by GB-InSAR. Most approaches for atmospheric correction are based on the homogenous atmospheric medium assumption that usually does not hold due to complex topography and various environmental factors, leading to low atmospheric correction accuracy. [...] Read more.

Atmospheric disturbance is a main interference for deformation monitoring by GB-InSAR. Most approaches for atmospheric correction are based on the homogenous atmospheric medium assumption that usually does not hold due to complex topography and various environmental factors, leading to low atmospheric correction accuracy. This study proposes two novel model-based approaches for non-homogenous atmospheric compensation in the azimuth and horizontal directions. The conception of a coordinate system is introduced to design the model for the first time. The 2D atmospheric compensation method designed based on the polar coordinate system can address the non-homogenous atmospheric phase screen (APS) correction in the azimuth direction. The 3D atmospheric compensation method based on the rectangular coordinate system deals with the non-homogenous APS in all three directions, and can better address the non-homogenous APS in the elevation direction than the 2D method. Compared with conventional models, the 2D and 3D models consider the other non-homogenous APS conditions in their respective coordinate systems, which helps to broaden the application of model-based approaches. Experiments using different equipment over two study areas are conducted to test the efficiency of the proposed models. The results demonstrate that the proposed approaches can eliminate non-homogenous atmospheric disturbance and enhance the accuracy of GB-InSAR atmospheric compensation, leading to great improvements in slope deformation estimation. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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21 pages, 6014 KiB

Open AccessArticle

Comprehensive Investigation of Capabilities of the Left-Looking InSAR Observations in Coseismic Surface Deformation Mapping and Faulting Model Estimation Using Multi-Pass Measurements: An Example of the 2016 Kumamoto, Japan Earthquake

by Ying-Hui Yang, Qiang Chen, Qian Xu, Jing-Jing Zhao, Jyr-Ching Hu, Hao-Liang Li and Lang Xu

Remote Sens. 2021, 13(11), 2034; https://doi.org/10.3390/rs13112034 - 21 May 2021

Cited by 4 | Viewed by 2370

Abstract

We here present an example of the 2016 Kumamoto earthquake with its coseismic surface deformation mapped by the ALOS-2 satellite both in the right- and left-looking observation modes. It provides the opportunity to reveal the coseismic surface deformation and to explore the performance [...] Read more.

We here present an example of the 2016 Kumamoto earthquake with its coseismic surface deformation mapped by the ALOS-2 satellite both in the right- and left-looking observation modes. It provides the opportunity to reveal the coseismic surface deformation and to explore the performance of the unusual left-looking data in faulting model inversion. Firstly, three tracks (ascending and descending right-looking and descending left-looking) of ALOS PALSAR-2 images are used to extract the surface deformation fields. It suggests that the displacements measured by the descending left-looking InSAR coincide well with the ascending right-looking track observations. Then, the location and strike angle of the fault are determined from the SAR pixel offset-tracking technique. A complicated four-segment fault geometry is inferred for explaining the coseismic faulting of the Kumamoto earthquake due to the interpretation of derived deformation fields. Quantitative comparisons between models constrained by the right-looking only data and by joint right- and left-looking data suggest that left-looking InSAR could provide comparable constraints for geodetic modelling to right-looking InSAR. Furthermore, the slip model suggests that the series of events are dominated by the dextral strike-slip with some normal fault motions. The fault rupture initiates on the Hinagu fault segment and propagates from southwest to northeast along the Hinagu fault, then transforms to Futagawa fault with a slip maximum of 4.96 m, and finally ends up at ~7 km NW of the Aso caldera, with a rupture length of ~55 km. The talent of left-looking InSAR in surface deformation detection and coseismic faulting inversion indicates that left-looking InSAR can be effectively utilized in the investigation of the geologic hazards in the future, same as right-looking InSAR. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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17 pages, 20017 KiB

Open AccessTechnical Note

Nonlocal Feature Selection Encoder–Decoder Network for Accurate InSAR Phase Filtering

by Liming Pu, Xiaoling Zhang, Liming Zhou, Liang Li, Jun Shi and Shunjun Wei

Remote Sens. 2022, 14(5), 1174; https://doi.org/10.3390/rs14051174 - 27 Feb 2022

Cited by 8 | Viewed by 2156

Abstract

Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and only use local phase information. The idea of nonlocal processing has been proven to be very effective [...] Read more.

Accurate interferometric phase filtering is an essential step in InSAR data processing. The existing deep learning-based phase-filtering methods were developed based on local neighboring pixels and only use local phase information. The idea of nonlocal processing has been proven to be very effective for improving the accuracy of interferometric phase filtering. In this paper, we propose a deep convolutional neural network-based nonlocal InSAR filtering method via a nonlocal phase filtering network (NL-PFNet) based on the encoder–decoder structure and nonlocal feature selection strategy. Thanks to the powerful phase feature extraction ability of the encoder–decoder structure and the utilization of nonlocal phase information, NL-PFNet can predict an accurately filtered interferometric phase after training using a large number of interferometric phase images with different noise levels. Experiments on both simulated and real InSAR data show that the proposed method significantly outperforms three traditional well-established methods and another deep learning-based method. Compared with the InSAR-BM3D filter and another deep learning-based method, the mean square error of the proposed method is 25% and 11% lower when processing simulated data, respectively, and when processing the real Sentinel-1 interferometric phase, the no-reference evaluation metric Q of the proposed method is 25% and 9% higher, respectively. In addition, the running time of the proposed method is tens of times less than that of the traditional filtering methods. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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15 pages, 10655 KiB

Open AccessTechnical Note

Identifying Potential Landslides by Stacking-InSAR in Southwestern China and Its Performance Comparison with SBAS-InSAR

by Lele Zhang, Keren Dai, Jin Deng, Daqing Ge, Rubing Liang, Weile Li and Qiang Xu

Remote Sens. 2021, 13(18), 3662; https://doi.org/10.3390/rs13183662 - 14 Sep 2021

Cited by 100 | Viewed by 6494

Abstract

Landslide disasters occur frequently in the mountainous areas in southwest China, which pose serious threats to the local residents. Interferometry Synthetic Aperture Radar (InSAR) provides us the ability to identify active slopes as potential landslides in vast mountainous areas, to help prevent and [...] Read more.

Landslide disasters occur frequently in the mountainous areas in southwest China, which pose serious threats to the local residents. Interferometry Synthetic Aperture Radar (InSAR) provides us the ability to identify active slopes as potential landslides in vast mountainous areas, to help prevent and mitigate the disasters. Quickly and accurately identifying potential landslides based on massive SAR data is of great significance. Taking the national highway near Wenchuan County, China, as study area, this paper used a Stacking-InSAR method to quickly and qualitatively identify potential landslides based on a total of 40 Sentinel SAR images acquired from November 2017 to March 2019. As a result, 72 active slopes were successfully detected as potential landslides. By comparing the results from Stacking-InSAR with the results from the traditional SBAS-InSAR (Small Baselines Subset) time series method, it was found that the two methods had a high consistency, with 81.7% potential landslides identified by both of the two methods. A detailed comparison on the detection differences was performed, revealing that Stacking-InSAR, compared to SBAS-InSAR may miss a few active slopes with small spatial scales, small displacement levels and the ones affected by the atmosphere, while it has good performance on poor-coherence regions, with the advantages of low technical requirements and low computation labor. The Stacking-InSAR method would be a fast and powerful method to qualitatively and effectively identify potential landslides in vast mountainous areas, with a comprehensive understanding of its specialty and limitations. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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10 pages, 5293 KiB

Open AccessTechnical Note

Quantitatively Estimating of InSAR Decorrelation Based on Landsat-Derived NDVI

by Yaogang Chen, Qian Sun and Jun Hu

Remote Sens. 2021, 13(13), 2440; https://doi.org/10.3390/rs13132440 - 22 Jun 2021

Cited by 15 | Viewed by 3872

Abstract

As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value [...] Read more.

As a by-product of Interferometric Synthetic Aperture Radar (SAR, InSAR) technique, interferometric coherence is a measure of the decorrelation noise for InSAR observation, where the lower the coherence value, the more serious the decorrelation noise. In the densely vegetated area, the coherence value could be too low to obtain any valuable signals, leading to the degradation of InSAR performance and the possible waste of expensive SAR data. Normalized Difference Vegetation Index (NDVI) value is a measure of the vegetation coverage and can be estimated from the freely available optical satellite images. In this paper, a multi-stage model is established to quantitatively estimate the decorrelation noise for vegetable areas based on Landsat-derived NDVI prior to the acquisition of SAR data. The modeling process is being investigated with the L-band ALOS-1/PALSAR-1 data and the Landsat-5 optical data acquired in the Meitanba area of Hunan Province, China. Furthermore, the reliability of the established model is verified in the Longhui area, which is situated near the Meitanba area. The results demonstrate that the established model can quantitatively estimate InSAR decorrelation associated with the vegetation coverage. Full article

(This article belongs to the Special Issue Advances in InSAR Imaging and Data Processing)

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