Interferometric Synthetic Aperture Radar Phase Research Articles

Coseismic Deformation of the 2023 Türkiye Earthquake Doublet from Sentinel-1 InSAR and Implications for Earthquake Hazard

Abstract The 6 February 2023 Türkiye earthquake doublet occurred on the east Anatolian fault system, which marks the tectonic boundary between the Arabia plate and the Anatolian microplate. This earthquake doublet consists of the Mw 7.8 Pazarcik earthquake along the east Anatolian fault and the Mw 7.6 Çardak earthquake along the Savrun–Çardak fault. Sentinel-1 Interferometric Synthetic Aperture Radar (InSAR) satellite successfully imaged the surface deformation caused by this earthquake doublet. The pixel offset from cross correlation of two Synthetic Aperture Radar images complements the interferograms in mapping the surface ruptures and the near-field deformation. We inverted for a coseismic slip model in elastic half-space using the InSAR phase and the range offset data. The variance reduction of the inversion reaches ∼90%. The coseismic slip model shows that the 2023 Türkiye earthquake doublet are left-lateral strike-slip events. The peak slip is located near Nurhak in southern Türkiye along the Savrun–Çardak fault. From measuring discontinuities in the pixel offset images we found that the surface rupture length of the Pazarcik earthquake is ∼300 km and the surface rupture length of the Çardak earthquake is ∼100 km. To first order, the faults are dipping vertically. “Slip gaps” are identified by our modeling, and they might be the source regions of future large earthquakes.

A U-Net Approach for InSAR Phase Unwrapping and Denoising

The interferometric synthetic aperture radar (InSAR) imaging technique computes relative distances or surface maps by measuring the absolute phase differences of returned radar signals. The measured phase difference is wrapped in a 2π cycle due to the wave nature of light. Hence, the proper multiple of 2π must be added back during restoration and this process is known as phase unwrapping. The noise and discontinuity present in the wrapped signals pose challenges for error-free unwrapping procedures. Separate denoising and unwrapping algorithms lead to the introduction of additional errors from excessive filtering and changes in the statistical nature of the signal. This can be avoided by joint unwrapping and denoising procedures. In recent years, research efforts have been made using deep-learning-based frameworks, which can learn the complex relationship between the wrapped phase, coherence, and amplitude images to perform better unwrapping than traditional signal processing methods. This research falls predominantly into segmentation- and regression-based unwrapping procedures. The regression-based methods have poor performance while segmentation-based frameworks, like the conventional U-Net, rely on a wrap count estimation strategy with very poor noise immunity. In this paper, we present a two-stage phase unwrapping deep neural network framework based on U-Net, which can jointly unwrap and denoise InSAR phase images. The experimental results demonstrate that our approach outperforms related work in the presence of phase noise and discontinuities with a root mean square error (RMSE) of an order of magnitude lower than the others. Our framework exhibits better noise immunity, with a low average RMSE of 0.11.

A Robust InSAR Phase Unwrapping Method via Improving the pix2pix Network

The main core of InSAR (interferometric synthetic aperture radar) data processing is phase unwrapping, and the output has a direct impact on the quality of the data processing products. Noise introduced from the SAR system and interferometric processing is unavoidable, causing local phase inaccuracy and limiting the unwrapping results of traditional unwrapping methods. With the successful implementation of deep learning in a variety of industries in recent years, new concepts for phase unwrapping have emerged. This research offers a one-step InSAR phase unwrapping method based on an improved pix2pix network model. We achieved our aim by upgrading the pix2pix network generator model and introducing the concept of quality map guidance. Experiments on InSAR phase unwrapping utilizing simulated and real data with different noise intensities were carried out to compare the method with other unwrapping methods. The experimental results demonstrated that the proposed method is superior to other unwrapping methods and has a good robustness to noise.

InDeandCoE: A framework based on multi-scale feature fusion and residual learning for interferometric SAR remote sensing image denoising and coherence estimation

Synthetic aperture radar (SAR) interferometry is a high-resolution microwave remote sensing imaging method. Over the past two decades, many researchers working on remote sensing have applied this technology in various disciplines, including environmental monitoring, disaster monitoring, and elevation mapping. However, due to the existence of many influencing factors in the acquisition stage, such as atmospheric humidity and temperature, the reflected wave signals from the ground will be disturbed when received by remote sensing satellites. The presence of noise in interferograms is inevitable. Therefore, the accuracy of interferometric SAR phase denoising and coherence estimation has a decisive impact on the validity of subsequent processing results. In this paper, we pioneer the use of a nested U-net as a feature extractor for interferometric SAR phase and coherence. In addition, we build a phase filter and a coherence estimator by using the residual learning module. With the aim of determining the unique non-local similarity of InSAR images, we use non-local convolution and channel attention mechanisms to extract features in different dimensions of the interferogram. Through quantitative and qualitative experiments, the proposed method performs better in phase denoising and coherence estimation than state-of-the-art methods.

Retrieving the Kinematic Process of Repeated-Mining-Induced Landslides by Fusing SAR/InSAR Displacement, Logistic Model, and Probability Integral Method

The extraction of underground minerals in hilly regions is highly susceptible to landslides, which requires the application of InSAR techniques to monitor the surface displacement. However, repeated mining for multiple coal seams can cause a large displacement beyond the detectable gradient of the traditional InSAR technique, making it difficult to explore the relationship between landslides and subsurface excavations in both temporal and spatial domains. In this study, the Tengqing landslide in Shuicheng, Guizhou, China, was chosen as the study area. Firstly, the large-gradient surface displacement in the line of sight was obtained by the fusion of SAR offset tracking and interferometric phase. Subsequently, a multi-segment logistic model was proposed to simulate the temporal effect induced by repeated mining activities. Next, a simplified probability integral method (SPIM) was utilized to invert the geometry of the mining tunnel and separate the displacement of the mining subsidence and landslide. Finally, the subsurface mining parameters and in situ investigation were carried out to assess the impact of mining and precipitation on the kinematic process of Tengqing landslides. Results showed that the repeated mining activities in Tengqing can not only cause land subsidence and rock avalanches at the top of the mountain, but also accelerate the landslide displacement. The technical approach presented in this study can provide new insights for monitoring and modeling the effects of repeated mining-induced landslides in mountainous areas.

An Extraction Method for Large Gradient Three-Dimensional Displacements of Mining Areas Using Single-Track InSAR, Boltzmann Function, and Subsidence Characteristics

This paper presents an extraction method for large gradient three-dimensional (3-D) displacements of mining areas using single-track interferometric synthetic aperture radar (InSAR), Boltzmann function, and subsidence characteristics. This is mainly aimed at overcoming the limitations of surface deformation monitoring in mining areas by using single-track InSAR technology. One is that the rapid and large gradient deformation of the mine surface usually leads to image decoherence, which makes it difficult to obtain correct deformation information. Second, the surface deformation monitored by InSAR is only one-dimensional line of sight (LOS) displacement, and thus it is difficult to reflect the real 3-D displacements of the surface. Firstly, the Boltzmann function prediction model (BPM) is introduced to assist InSAR phase unwrapping; thus the missing large gradient deformation phase of InSAR is recovered. Then, the subsidence characteristics in mining horizontal (or near-horizontal) coal seams are used as prior knowledge for theoretical derivation, and a 3-D displacement extraction model of coal seam mining with single-track InSAR is constructed. The feasibility of the method is verified by simulating LOS displacements with random noise and underestimation phenomenon caused by the large gradient deformation as InSAR observations. The results show that the root mean square error (RMSE) of 3-D displacements on the observation line calculated by the proposed method is 21.5 mm, 19.0 mm, and 32.9 mm, respectively. Based on the single-track Sentinel-1 images, the method in this paper was applied to the extraction of surface 3-D displacements in the Huaibei coal mine, and the experimental results show that the extracted 3-D displacements are in good agreement with that of measurement by the surface observation station. The proposed method can adapt to limited InSAR acquisitions and complex monitoring environments.

Robust Surface Deformation and Tropospheric Noise Characterization From Common-Reference Interferogram Subsets

Interferometric Synthetic Aperture Radar (InSAR) surface deformation estimates often suffer from tropospheric noise errors, and many study sites do not have GPS or in-situ measurements for validating InSAR surface deformation solutions. Here we present a method for characterizing both surface deformation and tropospheric noise from interferogram subsets. Choosing different subsets of interferograms that use a common-reference SAR scene allows us to quantify tropospheric noise and deformation signals. We demonstrate this method using 95 C-Band Sentinel-1 SAR scenes acquired over the Oman Ophiolite and 133 scenes over the Island of Hawaii. For the Oman case, our method suggests that there is no detectable deformation signal. In this scenario, the average of any subset of interferograms with a common-reference SAR scene estimates the tropospheric noise contribution on that reference SAR date. Achieving ~0.5 cm of uncertainty requires a subset size of 40 common-reference interferograms. The observed tropospheric noise is non-Gaussian, and follows a seasonal pattern. Propagation of tropospheric noise leads to up to ~5 cm of false deformation signal when deriving either a stacking or SBAS time series solution. For the Hawaii case, our method shows that the observed InSAR phase on the south flank of Kilauea is due to a secular deformation signal, while the phase over Mauna Loa is mostly associated with tropospheric noise. Our results are validated with independent GPS tropospheric zenith delay and surface deformation time series with sub-cm RMS errors.

An Unsupervised CNN-Based Multichannel Interferometric Phase Denoising Method Applied to TomoSAR Imaging

Tomographic synthetic aperture radar (TomoSAR) is an advanced SAR interferometric technique to retrieve 3D spatial information. However, the standard deviation in the reconstructed elevation could be high due to the noise in the interferometric phases, which makes the denoising filter crucial before tomographic reconstruction. In this paper, we propose an unsupervised multichannel SAR interferometric phase denoising method based on the Convolution Neural Network (CNN). It utilizes the Weighted Least-Squares (WLS) regularization combining with the covariance of multichannel interferometric phases to minimize the standard deviation of phase noise, which leads to the accurate and complete TomoSAR reconstruction. This network is trained by real SAR images and the results of both simulated and real observations verify the effectiveness of our proposed method.

A Sparse-Model-Driven Network for Efficient and High-Accuracy InSAR Phase Filtering

Phase filtering is a vital step for interferometric synthetic aperture radar (InSAR) terrain elevation measurements. Existing phase filtering methods can be divided into two categories: traditional model-based and deep learning (DL)-based. Previous studies have shown that DL-based methods are frequently superior to traditional ones. However, most of the existing DL-based methods are purely data-driven and neglect the filtering model, so that they often need to use a large-scale complex architecture to fit the huge training sets. The issue brings a challenge to improve the accuracy of interferometric phase filtering without sacrificing speed. Therefore, we propose a sparse-model-driven network (SMD-Net) for efficient and high-accuracy InSAR phase filtering by unrolling the sparse regularization (SR) algorithm to solve the filtering model into a network. Unlike the existing DL-based filtering methods, the SMD-Net models the physical process of filtering in the network and contains fewer layers and parameters. It is thus expected to ensure the accuracy of the filtering without sacrificing speed. In addition, unlike the traditional SR algorithm setting the spare transform by handcrafting, a convolutional neural network (CNN) module was established to adaptively learn such a transform, which significantly improved the filtering performance. Extensive experimental results on the simulated and measured data demonstrated that the proposed method outperformed several advanced InSAR phase filtering methods in both accuracy and speed. In addition, to verify the filtering performance of the proposed method under small training samples, the training samples were reduced to 10%. The results show that the performance of the proposed method was comparable on the simulated data and superior on the real data compared with another DL-based method, which demonstrates that our method is not constrained by the requirement of a huge number of training samples.

Adapting InSAR Phase Linking for Seasonally Snow-Covered Terrain

Interferometric synthetic aperture radar (InSAR) time series analysis of natural terrain allows for characterization of long-term geophysical trends over extended areas and, in the case of distributed scatterers (DSs), is significantly enhanced by methods that exploit the full complex-valued scattering statistics. Phase-linking (PL) estimators impose a phase-closure constraint in order to estimate the temporal wrapped-phase history of a DS directly from its complex backscatter sample coherence matrix. Some PL methods, such as the SqueeSAR and maximum-likelihood-estimator of Interferometric phase (EMI) estimators, rely on knowledge of the coherence magnitude matrix. The true coherence magnitude is <i>a priori</i> unknown and must therefore be estimated from the data. Bias in these estimated coherence magnitudes reduces PL performance when the true coherence magnitude is low. Many areas of the Earth are seasonally snow-covered and, for natural terrain, this leads to severe cross-season decorrelation. This poses a significant challenge for PL estimators due to bias of the near-zero cross-season coherence magnitude estimates. We introduce a clustering approach to mitigate the PL estimator bias problem that exploits the fact that in natural terrain, many DSs decorrelate similarly. This allows for averaging over large numbers of same-behaving DS, which provides robust debiasing of the coherence magnitudes used during PL. We apply our method to a RADARSAT-2 spotlight-mode InSAR dataset over a site in the western Canadian Arctic and demonstrate significant reductions in <i>a posteriori</i> phase variance when compared to existing PL methods.

PDNet: A Lightweight Deep Convolutional Neural Network for InSAR Phase Denoising

Interferometric phase denoising is a vital procedure for interferometric synthetic aperture radar (InSAR)-based remote sensing techniques because it can improve the accuracy of the final InSAR product. Here, we propose a deep convolutional neural network (DCNN)-based InSAR phase denoising method, abbreviated PDNet. Given an ideal wrapped phase, φ, the PDNet learns the self-similarity function of φ from the input interferogram. After training, the PDNet obtains filtered wrapped phases using the maximum-likelihood approach by exhausting all φs from –π to π. Unlike a boxcar-based filtering method, the PDNet does not consist of an “averaging operation” on the spatial domain, and the resolution loss and interferometric fringe distortion will not directly affect the PDNet result. Thus, the PDNet can be considered a nonlocal phase denoising approach. Analyses and results show that the PDNet is an almost near-real-time denoising algorithm. Its denoising accuracy is higher than that of the available model- and learning-based InSAR phase denoising methods.

The potential of synthetic aperture radar interferometry for assessing meltwater lake dynamics on Antarctic ice shelves

Abstract. Surface meltwater drains on several Antarctic ice shelves, resulting in surface and sub-surface lakes that are potentially critical for the ice shelf collapse. Despite these phenomena, our understanding and assessment of the drainage and refreezing of these lakes is limited, mainly due to lack of field observations and to the limitations of optical satellite imagery during polar night and in cloudy conditions. This paper explores the potential of backscatter intensity and of interferometric coherence and phase from synthetic aperture radar (SAR) imagery as an alternative to assess the dynamics of meltwater lakes. In four case study regions over Amery and Roi Baudouin ice shelves, East Antarctica, we examine spatial and temporal variations in SAR backscatter intensity and interferometric (InSAR) coherence and phase over several lakes derived from Sentinel-1A/B C-band SAR imagery. Throughout the year, the lakes are observed in a completely frozen state, in a partially frozen state with a floating ice lid and as open-water lakes. Our analysis reveals that the meltwater lake delineation is challenging during the melting period when the contrast between melting snow and lakes is indistinguishable. Despite this finding, we show using a combination of backscatter and InSAR observations that lake dynamics can be effectively captured during other non-summertime months. Moreover, our findings highlight the utility of InSAR-based observations for discriminating between refrozen ice and sub-surface meltwater and indicate the potential for phase-based detection and monitoring of rapid meltwater drainage events. The potential of this technique to monitor these meltwater change events is, however, strongly determined by the satellite revisit interval and potential changes in scattering properties due to snowfall or melt events.

Error Evaluation of L-Band InSAR Precipitable Water Vapor Measurements by Comparison with GNSS Observations in Japan

Interferometric synthetic aperture radar (InSAR) enables us to obtain precipitable water vapor (PWV) maps with high spatial resolution through the phase difference caused by refraction in the atmosphere. Although previous studies have evaluated the error level of InSARPWV observations, they validated it only with C-band InSARPWV observations. Since ionospheric disturbance seriously contaminates the InSAR phase in the case of the lower-frequency SAR system, it is necessary for a PWV error level evaluation correcting the ionospheric effect appropriately if we use lower-frequency SAR systems, such as the Advanced Land Observing Satellite-2 (ALOS-2). In this paper, we evaluated the error level of the L-band InSARPWV observation obtained from ALOS-2 data covering four areas in Japan. We compared the InSAR observations with global navigation satellite system (GNSS) atmospheric observations and estimated the L-band InSARPWV error value by utilizing the error propagation theory. As a result, the L-band InSARPWV absolute error reached 2.83 mm, which was comparable to traditional PWV observations. Moreover, we investigated the impacts of the seasonality, the interferometric coherence, and the height dependence on the PWV observation accuracy in InSAR.

The Use of InSAR Phase Coherence Analyses for the Monitoring of Aeolian Erosion

Aeolian erosion occurring in sand deserts causes significant socio-economical threats over extensive areas through mineral dust storm generation and soil degradation. To monitor a sequence of aeolian erosion in a sand desert area, we developed an approach fusing a set of remote sensing data. Vegetation index and Interferometric Synthetic Aperture Radar (InSAR) phase coherence derived from space-borne optical/SAR remote sensing data were used. This scheme was applied to Kubuqi Desert in Inner Mongolia where the effects of activity to combat desertification could be used to verify the outcome of the approach. We first established time series phase coherence and conducted a functional operation based on principal component analysis (PCA) to remove uncorrelated noise. Then, through decomposition of vegetation effect, where a regression model together with the Enhanced Vegetation Index (EVI) was employed, we estimated surface migration caused by aeolian interaction, that is, the aeolian erosion rate (AER). AER metrics were normalized and validated by additional satellite and ground data. As a result, the spatiotemporal migration of the target environment, which certainly induced dust storm generation, was traced and analyzed based on the correlations among surface characteristics. It was revealed that the derived AER successfully monitored the surface changes that occurred before and after the activities to combat desertification in the target area. Employing the established observation scheme, we expect a better understanding of the aeolian process in sand deserts with enhanced spatio-temporal resolution. In addition, the scheme will be beneficial for the evaluation of combating desertification activities and early warning of dust storm generations.

CVCMFF Net: Complex-Valued Convolutional and Multifeature Fusion Network for Building Semantic Segmentation of InSAR Images

Building segmentation of synthetic aperture radar (SAR) images is a challenging task that has not been solved well. High-resolution interferometric SAR (InSAR) images can provide delicate textures and interferometric phase images useful for building segmentation. However, current semantic segmentation networks in computer vision cannot be directly applied in InSAR building segmentation tasks to get good results because of the InSAR images’ particularity. In this article, we present a novel complex-valued convolutional and multifeature fusion network (CVCMFF Net) specifically for building semantic segmentation of InSAR images. This CVCMFF Net not only learns from the complex-valued SAR images but also considers multiscale and multichannel feature fusion. It can effectively segment the layover, shadow, and background on both the simulated InSAR building images and the real airborne InSAR images. The segmentation performance of CVCMFF Net is significantly improved compared with those of other state-of-the-art networks. By feature visualization, the feature extraction rule and feature fusion mechanism of the network are explored. We hope that the proposed network can be beneficial to InSAR phase filtering, phase unwrapping, and information extraction in urban areas.

Soil Moisture Change Monitoring from C and L-band SAR Interferometric Phase Observations

The soil moisture changes (Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>v</b></sub> ) have a significant influence on forestry, hydrology, meteorology, agriculture, and climate change. Interferometric synthetic aperture radar (InSAR), as a potential remote sensing tool for change detection, was relatively less investigated for monitoring this parameter. DInSAR phase (φ) is sensitive to the changes in soil moisture ( <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>v</b></sub> ), and thus, can be potentially used for monitoring Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>v</b></sub> . In this article, the relations between φ and Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>v</b></sub> over wheat, canola, corn, soybean, weed, peas, and bare fields were investigated using an empirical regression technique. To this end, dual-polarimetric C-band Sentinel-1A and quad-polarimetric L-band uninhabited aerial vehicle synthetic aperture radar (UAVSAR) airborne datasets were employed. The regression model showed the coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) of 40% to 56% and RMSE of 4.3 vol.% to 6.1 vol.% between the measured and estimated Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>v</b></sub> for different crop types when the temporal baseline (Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> ) was very short. As expected, higher accuracies were obtained using UAVSAR given its very short Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> and its longer wavelength with R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of 47% to 59% and RMSE of 4.1 vol.% to 6.7 vol.% for different crop types. However, using the Sentinel-1 data with the long Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</b> and shorter wavelength (5.6 cm), the accuracies of Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>v</b></sub> estimations decreased significantly. The results of this study demonstrated that using the φ information from Sentinel-1 data is a promising approach for monitoring Δ <b xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">M</b> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><b>v</b></sub> at an early growing season or before the crop starts growing, but using L-band SAR data and lower temporal baselines are recommended once the biomass increases.

Reconstructed Aeolian Surface Erosion in Southern Mongolia by Multi-Temporal InSAR Phase Coherence Analyses

The Gobi Desert in southern Mongolia has been identified as the strongest dust storm hot spot threatening public health and socio-economic activities in East Asian countries. Despite its significance, the complete mapping of the aeolian surface erosion in southern Mongolian remains unresolved because of the extensive region of interest that cannot be interpreted easily by conventional approaches. Therefore, in this study, we built a mapping scheme to define undergoing aeolian erosion and applied it over the southern Gobi Desert. The remote sensing approach applied here was based on an interferometric synthetic-aperture radar (InSAR) time series technique. A number of Sentinel-1 InSAR pairs that generate phase coherences for a certain period were synthesized via the means of principle component analysis (PCA) to extract the topographic persistence indicative of surface erosion rates. Validation analyses performed through inter-comparisons of phase coherence signals over landmark areas and residuals between global digital elevation models (DEMs) confirmed the reliability of outputs. The results revealed the geological lineaments in southern Mongolia confining sandy deposits and the sediment transportation pathways. Apparently, such bounded aeolian deposits and transportation mechanisms within geological structures have significantly contributed to dust generation in the Gobi Desert over southern Mongolia. In addition, this study demonstrated that the newly developed InSAR time series technique has great potential for identifying intensified land erosion and dust sources.

Explanation of InSAR Phase Disturbances by Seasonal Characteristics of Soil and Vegetation

Seasonal phase disturbances in satellite Interferometric Synthetic Aperture Radar (InSAR) measurements have been reported in other studies to suggest sub-centimetre land surface terrain motion. These have been interpreted in various ways because they correlate with multiple other (sub-)seasonal signatures of, e.g., clay swelling/shrinkage and groundwater level. Recent microwave radar studies mention the occurrence of phase disturbances in different soil types and soil moisture. This study further explored this topic by modeling phase disturbances caused by both soil and vegetation surface characteristics and aimed to interpret what their possible effects on InSAR-interpreted terrain motion is. Our models, based on fundamental microwave reflection and transmission theory, found phase disturbances caused by seasonal variation of soil and vegetation that have the same magnitude as interpreted seasonal land movement in earlier InSAR studies. We showed that small, temporal differences in soil moisture and vegetation can lead to relatively large phase disturbances in InSAR measurements. These disturbances are a result of waves having to comply with boundary conditions at the interface between media with different dielectric properties. The findings of this study explain the seasonal variations found in other InSAR studies and will therefore bring new insights and alternative explanations to help improve interpretation of InSAR-derived seasonal terrain motion.

DeepInSAR—A Deep Learning Framework for SAR Interferometric Phase Restoration and Coherence Estimation

Over the past decade, using Interferometric Synthetic Aperture Radar (InSAR) remote sensing technology for ground displacement detection has become very successful. However, during the acquisition stage, microwave signals reflected from the ground and received by the satellite are contaminated, for example, due to undesirable material reflectance and atmospheric factors, and there is no clean ground truth to discriminate these noises, which adversely affect InSAR phase computation. Accurate InSAR phase filtering and coherence estimation are crucial for subsequent processing steps. Current methods require expert supervision and expensive runtime to evaluate the quality of intermediate outputs, limiting the usability and scalability in practical applications, such as wide area ground displacement monitoring and predication. We propose a deep convolutional neural network based model DeepInSAR to intelligently solve both phase filtering and coherence estimation problems. We demonstrate our model’s performance using simulated and real data. A teacher-student framework is introduced to handle the issue of missing clean InSAR ground truth. Quantitative and qualitative evaluations show that our teacher-student approach requires less input but can achieve better results than its stack-based teacher method even on new unseen data. The proposed DeepInSAR also outperforms three other top non-stack based methods in time efficiency without human supervision.

InSAR Phase Denoising: A Review of Current Technologies and Future Directions

Nowadays, interferometric synthetic aperture radar (InSAR) has been a powerful tool in remote sensing by enhancing the information acquisition. During the InSAR processing, phase denoising of interferogram is a mandatory step for topography mapping and deformation monitoring. Over the last three decades, a large number of effective algorithms have been developed to do efforts on this topic. In this paper, we give a comprehensive overview of InSAR phase denoising methods, classifying the established and emerging algorithms into four main categories. The first two parts refer to the categories of traditional local filters and transformed-domain filters, respectively. The third part focuses on the category of nonlocal (NL) filters, considering their outstanding performances. Latter, some advanced methods based on new concept of signal processing are also introduced to show their potentials in this field. Moreover, several popular phase denoising methods are illustrated and compared by performing the numerical experiments using both simulated and measured data. The purpose of this paper is intended to provide necessary guideline and inspiration to related researchers by promoting the architecture development of InSAR signal processing.