Arc synthetic aperture radar (ArcSAR) is a ground-based remote imaging technology with the ability to cover wide fields of view. However, because its azimuth is formed by scanning angle rotation, the ...focusing of ArcSAR images is different from classical SAR focusing. Since the existing imaging methods cannot give a good balance between computational efficiency and accuracy, the wavenumber domain algorithm (WMA) could become an interesting alternative. Due to the fact that in ArcSAR imaging, the slant range measurement depends on a sine term of the scanning angle, no dedicated WMA-based approach for ArcSAR imaging has been formulated yet. The main challenge in this context is that the solution of the stationary phase point cannot be resolved explicitly via Fourier transform (FT). This article proposes a new wavenumber domain imaging method, which exploits the sine law in the process of solving the stationary phase point during FT along the direction of the received echo using the triangular relationship formed by the target, the radar, and the rotation center of radar and then obtains the exact phase error expression in range and angular wavenumber domain without any approximation of the slant range or scanning angle. Using this formulation, we develop the corresponding phase error compensation method and complete image focusing. Through point target simulation and experiments on real ArcSAR data, the effectiveness of this method is verified in terms of imaging accuracy and computational efficiency.
The prediction of land subsidence is a crucial step for early warning of urban infrastructure damage and timely remedy. However, the performance of most mathematical and empirical prediction models ...is often compromised by their large number of parameters, complex operational processes and sparsely measured values. Currently, the traditional neural network models are popular and effective, but they cannot accurately discover the characteristic changes of time series data. In this paper, a long short-term memory (LSTM) neural network was proposed to predict the land subsidence of time series Interferometric Synthetic Aperture Radar (InSAR). First, the Persistent Scatterer Interferometric Synthetic Aperture Radar (PS-InSAR) technique was utilized to monitor the time series land subsidence at Beijing Capital International Airport (BCIA) from 2005 to 2010 based on ENVISAT ASAR images with a descending orbit. The results were compared with the existing results to verify the reliability and then used to analyse the temporal and spatial characteristics of the time series land subsidence of the BCIA. Based on the time series InSAR deformation data, the LSTM neural network was used to establish the prediction model of time series InSAR, and the results were compared with those of the Multi-Layer Perceptron (MLP) and Recurrent Neural Network (RNN). The comparison results showed that the LSTM neural network was more accurate than the MLP and RNN on the point scale (the root mean square error was 4.60 mm and the mean absolute error was 3.18 mm), the correlation coefficients between the prediction results of the LSTM neural network and the real InSAR measurement results in 2007 and 2008 were 0.93 mm and 0.96 mm, respectively, indicating that LSTM neural network had better prediction performance. Eventually, based on the land subsidence data of time series InSAR from 2006 to 2010, the LSTM neural network was applied to predict the BCIA time series land subsidence in 2011. The results predicted that cumulative subsidence in September 2011 would reach a maximum of 350 mm. Therefore, the LSTM neural network is a potentially effective prediction method, which can replace numerical or empirical models in the absence of detailed hydrogeological data. Moreover, its prediction results can be used to assist decision-making, early warning and hazard relief.
Interferometric Synthetic Aperture Radar (InSAR) is widely used to measure deformation of the Earth's surface over large areas and long time periods. A common strategy to overcome coherence loss in ...long-term interferograms is to use multiple multilooked shorter interferograms, which can cover the same time period but maintain coherence. However, it has recently been shown that using this strategy can introduce a bias (also referred to as a “fading signal”) in the interferometric phase. We isolate the signature of the phase bias by constructing “daisy chain” sums of short-term interferograms of different length covering identical 1-year time intervals. This shows that the shorter interferograms are more affected by this phenomenon and the degree of the effect depends on ground cover types; cropland and forested pixels have significantly larger bias than urban pixels and the bias for cropland mimics subsidence throughout the year, whereas forests mimics subsidence in the spring and heave in the autumn. We, propose a method for correcting the phase bias, based on the assumption, borne out by our observations, that the bias in an interferogram is linearly related to the sum of the bias in shorter interferograms spanning the same time. We tested the algorithm over a study area in western Turkey by comparing average velocities against results from a phase linking approach, which estimates the single primary phases from all the interferometric pairs, and has been shown to be almost insensitive to the phase bias. Our corrected velocities agree well with those from a phase linking approach. Our approach can be applied to global compilations of short-term interferograms and provides accurate long-term velocity estimation without a requirement for coherence in long-term interferograms.
•The characteristics of the phase bias in InSAR data are investigated.•A mitigation strategy is proposed to estimate the correction in each interferogram.•The proposed strategy is simple and effective in addressing the phase bias.•The proposed method provides a close performance to a phase linking approach.
Synthetic Aperture Radar Interferometry (InSAR) provides an effective tool to study slow-moving landslides. However, InSAR observations are often contaminated by tropospheric artefacts due to spatial ...and temporal variations of atmospheric refractivity. Particularly, the topography-dependent stratified delays may introduce seasonal oscillation biases into InSAR-measured deformation time series under steep terrains, which cannot be removed by conventional spatial and temporal filtering. In this study we proposed two complementary approaches to correct the stratified tropospheric delays for time series InSAR analysis when studying single landslides. One is the Iterative Linear Model (ILM) as an improved version of the traditional Linear Model (LM). The other is to fuse tropospheric delays predicted by several global weather models (FDWM) with different temporal intervals and spatial resolutions. Both methods are integrated into the standard Small BAseline Subset (SBAS) time series analysis procedure. We evaluated the proposed methods in three landslide-prone areas in southwest China using Sentinel-1 datasets. The experimental results demonstrated that the ILM method removed the seasonal stratified delays mixed in deformation time series, unaffected by the deforming points. The FDWM method achieved an optimal combination of tropospheric delay predictions by four weather models, i.e. ERA-Interim, ERA5, HRES ECMWF, and MERRA-2. Validations using in-situ GPS measurements suggested that the original Root Mean Squared (RMS) values of interferometric phases declined by more than 35% after both ILM and FDWM corrections. The ILM had better performances than the FDWM to correct stratified delay for single landslides, whereas the FDWM can be an effective alternative when the ILM is inapplicable in case of limited coherent points.
•We found seasonal tropospheric delay signals in InSAR results over steep terrains.•The iterative linear fitting method is robust to the adverse impacts of moving points.•The fusion method optimally combines delays predicted by multiple weather models.•The modified SBAS approach removes seasonal fluctuations in deformation time series.•The GPS measurements validate the reliability and accuracy of the proposed methods.
The Daguangbao mega-landslide (China), induced by the 2008 Wenchuan earthquake (Mw=7.9), with an area of approximately 8km2, is one of the largest landslides in the world. Experts predicted that the ...potential risk and instability of the landslide might remain for many decades, or even longer. Monitoring the activity of such a large landslide is hence critical. Terrain Observation by Progressive Scans (TOPS) mode from the Sentinel-1 satellite provides us with up-to-date high-quality Synthetic Aperture Radar (SAR) images over a wide ground coverage (250×250km), enabling full exploitation of various InSAR applications. However, the TOPS mode introduces azimuth-dependent Doppler variations to radar signals, which requires an additional processing step especially for SAR interferometry. Sentinel-1 TOPS data have been widely applied to earthquakes, but the performance of TOPS data-based time series analysis requires further exploitation. In this study, Sentinel-1 TOPS data were employed to investigate landslide post-seismic activities for the first time. To deal with the azimuth-dependent Doppler variations, a processing chain of TOPS time series interferometry approach was developed. Since the Daguangbao landslide is as a result of the collapse of a whole mountain caused by the 2008 Mw 7.9 Wenchuan earthquake, the existing Digital Elevation Models (DEMs, e.g. SRTM and ASTER) exhibit height differences of up to approximately 500m. Tandem-X images acquired after the earthquake were used to generate a high resolution post-seismic DEM. The high gradient topographic errors of the SRTM DEM (i.e. the differences between the pre-seismic SRTM and the actual post-seismic elevation), together with low coherence in mountainous areas make it difficult to derive a precise DEM using the traditional InSAR processing procedure. A re-flattening iterative method was hence developed to generate a precise TanDEM-X DEM in this study. The volume of the coseismic Daguangbao landslide was estimated to be of 1.189±0.110×109m3 by comparing the postseismic Tandem-X DEM with the preseismic SRTM DEM, which is consistent with the engineering geological survey result. The time-series results from Sentinel-1 show that some sectors of the Daguangbao landslide are still active (and displaying four sliding zones) and exhibiting a maximum displacement rate of 8cm/year, even eight years after the Wenchuan earthquake. The good performance of TOPS in this time series analysis indicates that up-to-date high-quality TOPS data with spatiotemporal baselines offer significant potential in terms of future InSAR applications.
•InSAR is used to investigate landslide post-seismic activities for the first time.•The current activity and the volume of the Daguangbao mega-landslide are presented.•A Sentinel-1 TOPS data based InSAR time series processing chain is developed.•A re-flattening iterative method is developed to generate precise DEMs.
Most of the developed groundwater basins in Iran are subject to land subsidence hazards resulting from the over-extraction of groundwater. Several areas in Tehran, the capital city and a provincial ...center in north-central Iran, have been reported to be subsiding at different rates. In this study, we present the results of an Interferometric Synthetic Aperture Radar (InSAR) time series analysis of Tehran using different SAR data between 2003 and 2017. By constructing more than 400 interferograms derived from 39 Envisat ASAR (C-band), 10 ALOS PALSAR (L-band), 48 TerraSAR-X (X-band), and 64 Sentinel-1 (C-band) SAR datasets, we compile displacement time series from interferometric observations using the Small Baseline (SB) technique. Our analysis identifies 3 distinct subsidence features in Tehran with rates exceeding 25 cm/yr in the western Tehran Plain, approximately 5 cm/yr in the immediate vicinity of Tehran international airport, and 22 cm/yr in the Varamin Plain to the southeast of Tehran city. The temporal pattern of land subsidence, which is dominated by a decreasing trend, generally follows the regional decline in groundwater level, which suggests that anthropogenic processes caused by excessive groundwater extraction are the primary cause of land subsidence. Integrating a decadal time series of subsidence constructed from multi-sensor InSAR with in-situ observations suggests that inelastic and permanent compaction dominates the main deformation regime of the Tehran aquifer, and the ratio between elastic and inelastic deformation is approximately 0.4. A geological analysis indicates that the shape of the subsidence bowl in the western Tehran Plain does not follow the trend of major mapped faults in the region. In contrast, the subsidence bowl in Varamin is controlled by the Pishva Fault, which suggests that either this fault acts as a hydrologic barrier to the groundwater flow in this region or that the differences in sediment thickness causes the discontinuity in land subsidence.
•Investigation of land subsidence in Tehran, Iran using multi-sensor SAR data•Long-term displacement time-series obtained by combining different SAR sensors•Long-term results show subsidence has been growing towards urban area of Tehran.•Time-varying characteristics of time-series are analyzed by wavelet transform.•The ratio between elastic and inelastic deformation is calculated.
Classification and identification of the materials lying over or beneath the earth's surface have long been a fundamental but challenging research topic in geoscience and remote sensing (RS), and ...have garnered a growing concern owing to the recent advancements of deep learning techniques. Although deep networks have been successfully applied in single-modality-dominated classification tasks, yet their performance inevitably meets the bottleneck in complex scenes that need to be finely classified, due to the limitation of information diversity. In this work, we provide a baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework. In particular, we also investigate a special case of multi-modality learning (MML)-cross-modality learning (CML) that exists widely in RS image classification applications. By focusing on "what," "where," and "how" to fuse, we show different fusion strategies as well as how to train deep networks and build the network architecture. Specifically, five fusion architectures are introduced and developed, further being unified in our MDL framework. More significantly, our framework is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs). To validate the effectiveness and superiority of the MDL framework, extensive experiments related to the settings of MML and CML are conducted on two different multimodal RS data sets. Furthermore, the codes and data sets will be available at https://github.com/danfenghong/IEEE_TGRS_MDL-RS , contributing to the RS community.
Phase unwrapping (PhU) is an important step in interferometric synthetic aperture radar (InSAR) technology. At present, difficulties are encountered when using deep learning to solve the PhU problem ...because the fringe density of the actual interferogram varies, resulting in an imbalanced class of semantic segmentation. Deep learning cannot completely use gradient information, and it is difficult to address a large number of residues. In this letter, a PhU semantic segmentation model based on gradient information fusion and improved PhaseNet network is proposed to solve the problem of imbalanced classification and error propagation. 21 613 pairs of phase samples are constructed by using simulated and real Sentinel-1 InSAR Data. The experimental results show that the average classification accuracy of the method can reach 97%, and the mean square error is only 0.97. The average processing speed of <inline-formula> <tex-math notation="LaTeX">256 \times256 </tex-math></inline-formula> slices is only 0.5 s. Compared with the traditional methods and other deep learning methods, this method solves the problem of classification imbalance, and the use of fusion gradient information improves the efficiency of the algorithm as well as reduces the burden of network classification and the error propagation, showing increased robustness in the case of many residues and high fringe density.
The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is generally composed of the extraction of a set of features that transform the raw input into a representation, ...followed by a trainable classifier. The feature extractor is often hand designed with domain knowledge and can significantly impact the classification accuracy. By automatically learning hierarchies of features from massive training data, deep convolutional networks (ConvNets) recently have obtained state-of-the-art results in many computer vision and speech recognition tasks. However, when ConvNets was directly applied to SAR-ATR, it yielded severe overfitting due to limited training images. To reduce the number of free parameters, we present a new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set illustrate that A-ConvNets can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.
Underground coal mining often causes subsidence, goaf landslides, fissures, and even hazard chains, seriously damaging the ecological environment. To address the ecological vulnerability of mining ...areas is key to exploring the development characteristics and failure mechanisms of surface multi-hazards. Taking the Shuiliandong coal mine as an example, the mine deformation area was identified using differential interferometric synthetic aperture radar (D-InSAR) technology. We found that the spatial evolution of the deformation areas was controlled by the mining sequence. The impacts on the surface deformation and the mining-induced landslide were continuous and long term. Thus, the surface fissures and landslide were characterized using historical images. The fissures were clustered and short, and there was a negative exponential relationship between the length and the cumulative number of fissures. The fissure density decreased with increasing distance from the mine tunnel. In addition, the particle flow numerical simulation analysis method was used to simulate the subsidence-fissure-landslide hazard chain process. Three distinct stages were identified: initial stage, rapid development stage, and creep stage. The displacements at the different monitoring points exhibited a distinct S shape. The cumulative number of fissures developed same as the subsidence and landslide, exhibiting an S shape. The fissures played an important role in the hazard chain, accelerating the subsidence and landslide processes.
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