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Abstract Background Exploring the impact of professional identity on the academic performance of nursing students is crucial for understanding how to improve educational outcomes in this field. Professional identity not only shapes students’ self-concept but also influences their motivation, commitment, and success in their studies. However, the mechanisms underlying this relationship are not fully elucidated. This study aims to examine the potential mediating roles of general self-efficacy and learning engagement in this relationship. Methods A total of 1097 Chinese nursing students participated in this study. They were assessed using the Professional Identity Questionnaire for Undergraduate Students, the General Self-Efficacy Scale, the Utrecht Work Engagement Scale for Students, and the College Students Academic Achievement Scale. The data were analyzed using Pearson correlation, structural equation modeling, and deviation-corrected percentile bootstrap techniques. Results (1) Professional identity (PI) was positively correlated with academic achievement (AA)(r = 0.446, P

This paper is intended to summarize the research conducted during the first 2 years of the Dragon 5 project 59,332 (geophysical and atmospheric retrieval from Synthetic Aperture Radar (SAR) data stacks over natural scenarios). Monitoring atmospheric phenomena, encompassing both tropospheric and ionospheric conditions, holds pivotal significance for various scientific and practical applications. In this paper, we present an exploration of advanced techniques for estimating tropospheric and ionospheric phase screens using stacks of Synthetic Aperture Radar (SAR) images. Our study delves into the current state-of-the-art in atmospheric monitoring with a focus on spaceborne SAR systems, shedding light on their evolving capabilities. For tropospheric phase screen estimation, we propose a novel approach that jointly estimates the tropospheric component from all the images. We discuss the methodology in detail, highlighting its ability to recover accurate tropospheric maps. Through a series of quantitative case studies using real Sentinel-1 satellite data, we demonstrate the effectiveness of our technique in capturing tropospheric variability over different geographical regions. Concurrently, we delve into the estimation of ionospheric phase screens utilizing SAR image stacks. The intricacies of ionospheric disturbances pose unique challenges, necessitating specialized techniques. We dissect our approach, showcasing its capacity to mitigate ionospheric noise and recover precise phase information. Real data from the Sentinel-1 satellite are employed to showcase the efficacy of our method, unraveling ionospheric perturbations with improved accuracy. The integration of our techniques, though presented separately for clarity, collectively contributes to a comprehensive framework for atmospheric monitoring. Our findings emphasize the potential of SAR-based approaches in advancing our knowledge of atmospheric processes, thus fostering advancements in weather prediction, geophysics, and environmental management.

ABSTRACTDue to the strong penetrability, long-wavelength synthetic aperture radar (SAR) can provide an opportunity to reconstruct the three-dimensional structure of the penetrable media. SAR tomography (TomoSAR) technology can resynthesize aperture perpendicular to the slant-range direction and then obtain the tomographic profile consisting of power distribution of different heights, providing a powerful technical tool for reconstructing the three-dimensional structure of the penetrable ground objects. As an emerging technology, it is different from the traditional interferometric SAR (InSAR) technology and has advantages in reconstructing the three-dimensional structure of the illuminated media. Over the past two decades, many TomoSAR methods have been proposed to improve the vertical resolution, aiming to distinguish the locations of different scatters in the unit pixel. In order to cope with the forest mission of European Space Agency (ESA) that is designed to provide P-band SAR measurements to determine the amount of biomass and carbon stored in forests, it is necessary to systematically evaluate the performance of forest height and underlying topography inversion using TomoSAR technology. In this paper, we adopt three typical algorithms, namely, Capon, Multiple Signal Classification (MUSIC), and Compressed Sensing (CS), to evaluate the performance in forest height and underlying topography inversion. The P-band airborne full-polarization (FP) SAR data of Lopè National Park in the AfriSAR campaign implemented by ESA in 2016 is adopted to verify the experiment. Furthermore, we explore the effects of different baseline designs and filter methods on the reconstruction of the tomographic profile. The results show that a better tomographic profile can be obtained by using Hamming window filter and Capon algorithm in uniform baseline distribution and a certain number of acquisitions. Compared with LiDAR results, the root-mean-square error (RMSE) of forest height and underlying topography obtained by Capon algorithm is 2.17 m and 1.58 m, which performs the best among the three algorithms.

BackgroundDepressive symptoms represent a significant public health challenge, impacting the mental well-being of older adults. Despite this, the understanding of how activities of daily living (ADL) abilities correlate with life satisfaction and depressive symptoms among older Chinese adults remains limited.AimsThis study aims to investigate the relationship between ADL and depressive symptoms in older people Chinese individuals, with a specific focus on examining the mediating role of life satisfaction within this context.MethodsThe study utilized data from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2020. A cohort of 8,343 individuals aged 60 years and above was included. The analysis was conducted using STATA 17.0 and SPSS 26.0, employing descriptive statistics, chi-square tests, Pearson correlations, and mediation analysis using the percentile Bootstrap method with 5,000 resamples to explore the interrelations among ADL, life satisfaction, and depressive symptoms.ResultsADL is positively correlated with life satisfaction (r = 0.129, p

As a world-class problem that has confused the field of geoscience for a long time, the formation mechanism of the peculiar feather-shaped dunes in Kumtag Desert has not been clarified due to the deficiency of a comprehensive investigation on the dune dynamic characteristics. Because of the quickly changeable dune morphology and sands’ low dielectric constants, the traditional single technique generally was only used to identify or quantify dune mobility in one or two dimensions, and cannot retrieve the complete information of dune dynamic characteristics. To overcome these deficiencies, a comprehensive study of confusing the optical image cross-correlation, synthetic aperture radar (SAR) offset-tracking, and small baseline subsets interferometric SAR (InSAR) and InSAR coherence was performed here to identify and quantify the movements of feather-shaped dunes from a mutidimensional (East–West, North–South, slant-range, azimuth, and coherence) perspective. These results showed that the feather-shaped dunes demonstrate a movement tendency from the northeastern to the southwestern orientation (204.32° ± 52.15°) with the displacement magnitude of 3.34 ± 2.26 m/year, in consistent with the predominant wind condition. Specifically, the spatial heterogeneity was found that these dunes move along a relatively small slope gradient (0°–6°) and a northwestern–southeastern facing slope aspect (300°–350°, 120°–180°). Besides, the large displacement velocities are mainly concentrated in the pinna rachis rather than the rami of the feather-shaped dunes. This article sheds a light on the comprehensive investigation on the large-scale dynamic characteristics of peculiar feather-shaped dunes by combining the medium-resolution optical and SAR satellite observations.

On July 20, 2021, the northern Henan Province was hit by a historically rare, exceptionally heavy rainstorm (“7.20” Rainstorm Event), accompanied by severe urban flooding, flash floods, landslides, and other multiple disasters, resulting in significant casualties and property losses. On the other hand, the long-term overexploitation of groundwater since the last century has led to severe ground subsidence in the same area. We apply the intensity information of Sentinel-1 SAR images to extract the large-scale flood inundation area and their phase information to measure the ground deformation. Since heavy precipitations can recharge groundwater, the relationship between flood inundation, groundwater level change, and ground deformation is analyzed. The results show that the flood inundation areas are mainly distributed along the major rivers due to river overflowing, while heavy precipitation led to the rise of groundwater levels, and there was a significant amount of subsidence mitigation and surface uplift across the region due to the groundwater recovery. This study demonstrates the contribution of radar remote sensing to analyze the mechanism of groundwater recharge and subsidence mitigation benefited by rainstorm events and provides a technical reference to similar circumstances.

With the combined effects of climate change and anthropogenic disturbance, landslide hazards have progressively increased and emerged as one of the most significant natural threats to socio-economic safety and human life. Synthetic aperture radar interferometry (InSAR) can measure subtle ground displacement and thus has immense potential for detecting active landslides. However, the operational application of InSAR for landslide detection and inventory update in wide-area is still hindered by the high labor and time costs for visual interpretation and manual editing of InSAR results. Aiming at this problem, we developed a novel method using InSAR and convolutional neural network (CNN) for automated identification of active landslides over wide areas. It first performs InSAR analysis to produce a surface displacement velocity map of the target region and then employs an improved Faster RCNN based on attended ResNet-34 and Feature Pyramid Networks (FPN) to detect active landslides from the velocity map. Taking the Guizhou province in southwest China as a case study, we processed 1168 scenes of Sentinel-1 images and 473 scenes of PALSAR-2 images to derive the surface displacement and identified 1627 active landslides, including 326 manually labeled landslides and 1301 landslides automatically detected by Faster RCNN. The improved Faster RCNN achieved good recall, precision, F1 score, and average precision (AP) at 91.49%, 91.33%, 0.914, and 0.940, respectively. Further experiments indicated that the trained Faster RCNN showed satisfactory applicability and result accuracy for different test areas and various InSAR techniques. Therefore, the proposed approach has great potential applications for building inventories of active landslides over wide areas and regularly updating the records, which is crucial for preventing landslide disasters and mitigating losses.

Despeckling is an essential task in polarimetric synthetic aperture radar (PolSAR) image processing. Most of the existing filters developed for multitemporal synthetic aperture radar (SAR) images make use of either real or complex information. Real information refers to amplitude or intensity values, whereas complex information refers to complex covariance matrix (CCM) derived from either interferometric SAR (InSAR) or PolSAR data. The InSAR CCM is formed using images of the same polarimetric channel but acquired at different dates, and the PolSAR CCM contains information acquired simultaneously in different polarimetric channels. Therefore, these despeckling methods may present good performance in some applications and scenes but fail in other cases, due to differences in input sources. In order to achieve a more robust result in all cases, we develop a method for multitemporal polarimetric SAR data filtering based on tensor decomposition, which aims at improving the identification of homogeneous pixels for spatially adaptive filtering. The key element of this approach consists of exploiting tensor theory to construct a new CCM that contains both polarimetric and interferometric information, as well as multitemporal information for each pixel. The effectiveness of the proposed method and its performance are evaluated with simulated and real SAR data in comparison with several established methods.

Land subsidence can be observed with time-series of Interferometric Synthetic Aperture Radar (InSAR) data. However, existing approaches only reveal subsidence signals that are multi-scale mixed, which is not conducive to the systematic analysis of subsidence of different mechanisms. A deformation signal decomposition (DSD) method based on spectral analysis is used to decompose the deformation extracted by time-series InSAR into three classes of deformation signals. They refer to large-scale deformation related to geological settings, medium-scale deformation caused more by group excavation, and small-scale deformation along linear infrastructures. TerraSAR-X datasets for Shanghai spanning April 2013 to September 2020, and Sentinel-1A datasets spanning January 2016 to September 2020 are used in this study. The results were cross-verified between the TerraSAR-X and Sentinel-1A datasets, and validated against levelling measurements. Subsidence signals caused by different mechanisms were automatically decomposed, which facilitates a systematic analysis for targeted diagnosis of land subsidence signals. A detailed analysis was conducted jointly at three scales of surface displacement, geological conditions, major construction activities, and subsidence mechanisms. It indicated that construction activities were the leading cause of land subsidence, and suggests that local authorities that wish to mitigate surface subsidence may benefit from primarily considering this process.

Surface monitoring of artificial canals is essential to ensure safe operation. Traditional geodetic tools are commonly employed to monitor the canal of China’s South-to-North Water Diversion Project (SNWDP), which is time-consuming and high-cost. The advanced satellite Synthetic Aperture Radar Interferometry (InSAR) is an effective tool to detect subtle surface deformation, while its application for monitoring canals is short of fine deformation interpretation on a structure scale. In this study, we applied multiple SAR satellites to monitor a typical canal of the Middle Route of the SNWD Project (MRP), the Changge Canal, by extracting the historical deformation, inverting two-dimensional (2D) deformation, and analyzing distortions. The result suggests that the focused canal section is experiencing continuous deformation caused by near coal mining activity. Its vertical and horizontal distortions both reach ±1.5 mm/100 m. The InSAR applicability, including visibility and sensitivity, is applied to the structure-level interpretation of deformation. The InSAR MPs distribution highly depends on canal structures' aspect and surface coverings. The canal structures with high coherence and lower R-Index (i.e., foreshortening) are favorable for detecting InSAR MPs; meanwhile, a few MPs are detected for the structures with high coherence but high R-index. For the sensitivity of the deformation vector along the downslope, its dependency on the aspect opposes InSAR MPs distribution. The structure-level interpretation considering InSAR applicability helps deploy targeted maintenance and analyze deformation mechanisms. The procedure of fine InSAR monitoring applies to similar water diversion projects.

There is an urgent demand for continuous detection and monitoring of active slopes in wide reservoir areas, as reservoir impoundments may activate unstable slopes. Although satellite time-series InSAR has been widely used in mapping active landslides, the tedious artificial interpretation of InSAR results limits the efficiency and reliability of landslides detection. We propose a set of procedures for deformation monitoring and continuous automatic landslide identification in wide reservoir areas. The local time window estimation can extract the nonlinear deformation in the time series InSAR signal, thus enhancing the deformation field. Spatial adaptive clustering enables the effective extraction of unstable slopes. The abnormal deformation trends of landslides can be updated through the continuous identification of new results and comparison with historical results. The procedure is used to continuously detect unstable slopes in the Lianghekou reservoir area before and after the impoundment. Combining the ascending and descending SAR data of Sentinel-1, 109 historically active landslides were found and 18 new landslides were activated within one year after the first water storage. The distribution and types of unstable slopes were analyzed, followed by two case studies. The first case shows the different effects of the two-stage water storage on slope deformations, indicating rising water levels' critical role in landslide activation. The second case shows the consolidation settlement of the embankment dam and the influence of water storage on slope deformation monitoring. The results demonstrate the effectiveness of the InSAR automatic landslide identification and this study provides a technical reference for similar reservoir area.

The Middle Route of South-to-North Water Diversion (SNWD) Project,known as one of the greatest hydraulic projects of China, undertakes most of domestic and industrial water use in the Beijing–Tianjin area, as well as a part of ecological water use. The whole canal should be monitored to ensure a continuous water supply for water-receiving areas. Compared with traditional ground-based monitoring tools limited by the low distribution density, intensive labor force, and huge cost, the satellite synthetic aperture radar interferometry (InSAR) with wide coverage, high-frequency revisiting, and low cost is more suitable for monitoring large-scale infrastructures. In this study, we employed the Sentinel-1 data to monitor the canal of the Middle Route of the SNWD Project within Henan Province. The deformation rates along the canal were obtained by using persistent scatterer InSAR, from which a total of 20 deformed canal sections and seven suspected deformed canal sections were identified. Among them, the Shahe aqueduct is overall stable except for the Shahe Beam Aqueduct and Lushanpo Landing Aqueduct. The Yuzhou-Changge canal passes through a subsidence funnel with a maximum deformation rate of about –20 mm/year. The InSAR-derived deformation coincides well with the in-situ leveling measurements over the Yuzhou-Changge canal. Their correlation is 0.95 and the root mean square error of their differences is 2.41 mm/year. The results demonstrate the effectiveness of satellite InSAR for monitoring large-scale hydraulic engineering. It can be combined with ground leveling to achieve overall investigations and detailed monitoring, largely improving the efficiency and cost of the current ground monitoring system.

A flood event induced by heavy rainfall hit the Taiyuan basin in north China in early October of 2021. In this study, we map the flood event process using the multitemporal synthetic aperture radar (SAR) images acquired by Sentinel-1. First, we develop a spatiotemporal filter based on low-rank tensor approximation (STF-LRTA) for removing the speckle noise in SAR images. Next, we employ the classic log-ratio change indicator and the minimum error threshold algorithm to characterize the flood using the filtered images. Finally, we relate the flood inundation to the land subsidence in the Taiyuan basin by jointly analyzing the multitemporal SAR change detection results and interferometric SAR (InSAR) time-series measurements (preflood). The validation experiments compare the proposed filter with the Refined-Lee filter, Gamma filter, and a statistically homogeneous pixels selection based multitemporal SAR filter. The results demonstrate the effectiveness and advantage of the proposed STF-LRTA method in SAR despeckling and detail preservation, and the applicability to change scenes. The joint analyses reveal that land subsidence might be an important contributor to the flood event, and the flood recession process linearly correlates with time and subsidence magnitude.

The metro system solves traffic congestion in urban transportation while bringing potential deformation risks, especially in tunnels built in typical soft deposits in coastal cities. Time-series Synthetic Aperture Radar Interferometry (TS-InSAR) is an effective tool for monitoring land subsidence in urban areas. However, the different structures of metro lines, such as elevated tracks, ground lines, and tunnels, were often neglected in previous studies. Here, we introduced a Station Based Monitoring (SBM) method combined with InSAR analyses for metro safety monitoring. TS-InSAR provided the measurements along the elevated rails and ground lines. For tunnels, the TS-InSAR and SBM were jointly performed to deduct partial deformation under tunnels caused by land subsidence. The proposed metro system monitoring strategy was demonstrated and validated over the Shanghai Metro and Tianjin Rail Transit in the coastal cities of China. The results show that most deformed sections are located on the metro network's periphery, with velocities around −5 mm/year in Shanghai and −25 mm/year in Tianjin. The spatial distribution and temporal evolution of deformation characteristics along the two cases were then explored and compared. The proposed hazard matrix combining geological conditions and passenger density provided a more comprehensive assessment of potential hazards. The study highlights that InSAR and SBM analyses could be potentially applied to monitoring metro systems considering different structures.

Interferometry and pixel offset tracking (POT) are two major technologies to measure ground deformation with repeat-pass synthetic aperture radar (SAR) observations. The SAR POT method can tolerate a much larger deformation gradient than interferometric SAR (InSAR), and thus it has been widely applied in monitoring earthquakes, volcanoes, landslides, glaciers, etc. However, there is an intrinsic trade-off between matching accuracy and detail preserving in terms of matching window size for the traditional SAR POT method using only single-polarization intensity. Taking advantage of polarimetric scattering properties, this study develops a novel POT method, named PCM-POT, towards mitigating the dependence of offset estimations on matching window size. It employs a generalized likelihood ratio test (GLRT) upon polarimetric covariance matrices (PCMs) to evaluate the similarity of two candidate pixels and estimate high-resolution surface displacements with high accuracy. The effectiveness and advantage of PCM-POT are demonstrated by an experiment of retrieving the displacements of the Slumgullion landslide from a pair of full-polarimetric UAVSAR images. Comparisons with the traditional POT indicate that using polarimetric scattering properties can reduce the uncertainty in offset estimations over heterogeneous sliding areas. Further experiments suggest that PCM-POT is also suitable for dual-polarimetric satellite SAR data.

Quantifying the kinematic evolution patterns of mountain glaciers near Yarlung Tsanpo River performs a major role in evaluating the glacial instability and the secondary disasters. For the Sedongpu Basin near the Yarlung Tsanpo River Valley, the dramatic geomorphic landscape variations triggered by the ice-rock avalanche events were visually identified as the dominant texture deficiencies in time-series optical images. To improve the image correlation quality broken by these image texture deficiencies, the Landsat-8/Sentinel-2 optical images were divided into different groups, then a stepwise combination strategy was innovatively proposed to derive the glacier time-series displacement velocities in different temporal stages. The standard deviations (STD) of the optical measurements in the stable area maintained around 0.04 m/yr for the normalized displacement velocity and maintained from 0.6 to 1.7 m for the cumulative displacement time series. The obvious variations in glacier displacement velocity were identified before each collapse event. Subsequently, the offset-tracking procedures were performed on 7 Sentinel-1A Synthetic Aperture Radar (SAR) images to acquire the range and azimuth displacement velocities. To better reveal the dynamic mechanism of the glacier activity, the three-dimensional (3D) glacial displacement velocity was also derived by using optical and SAR results. The precipitation, temperature, and seismic activities were assumed as the main triggering factors of controlling the glacial dynamic mechanism and final collapse events. Additionally, the dynamic mechanism of the active glaciers in Sedongpu Basin conformed to a power law, which was limited by the changes of the internal stress friction force on the sliding base surface. The aim of this study is to shed a light on interpreting the precursory displacement patterns and their implicit failure mechanism of these ice-rock avalanche events with the conventional freely optical and SAR observations.

Quantifying the kinematic evolution patterns of dune migration plays a major role in resisting the sand-dust disasters and evaluating the desertification process. With the increment of optical observations, measuring the dune migration with dense spatial and temporal density has become possible. In this study, with the open-accessible Landsat-8 (L8)/Sentinel-2 (S2) imagery and global terrain datasets, we designed a complete workflow of inverting the dune displacement time series and velocity, identifying the potential encroachment risk, and evaluating the sediment balance of mobilized dune fields near Dunhuang Oasis. After the performance of time-series inversion, the standard deviation (STD) of measurements in stable area of raw correlation results was improved by above 40%. The displacement time series and velocity revealed out the spatial heterogeneity and seasonal/inter-annual dynamic evolution of dune migration. Furthermore, dune encroachment risk in the immediate vicinity of local landscapes was evaluated to highlight the unstable dune fields, and the workability of existent sand-control facilities. Additionally, the mass budget of around −0.001 ± 0.025 m/yr shows the stable sediment transport in Mingsha mountain. This study case can be exemplified to lead the risk monitoring and sustainable conservation of Dunhuang region and other places controlled by similar climatic conditions.

Atmospheric propagation delay correction is the key to improving the accuracy of deformation measurement of satellite interferometric synthetic aperture radar (InSAR). The empirical phase-elevation models and external data-based models present uneven performances of atmospheric delay correction for InSAR deformation monitoring. In this study, based on our previous fusion of delays predicted by multiple weather models (FDWM), we propose a new approach of adaptive fusion of multi-source tropospheric delay (AFMTD) estimates derived from multiple models over wide areas, i.e., ERA5, GACOS, WRF, MERRA2, NARR, MODIS, Linear model, and Powerlaw model. The spatially varying scaling algorithm is employed to refine the tropospheric delays predicted by the weather models. Meanwhile, we adopt a multiple-window strategy to cope with the spatially lateral variation of tropospheric delays. The AFMTD not only improves the spatial heterogeneity of tropospheric delay, but also adaptively combines multiple models to achieve a more reliable delay estimation. This AFMTD method is incorporated into the StaMPS-SBAS procedure. We compared the AFMTD with other single models using ENVISAT ASAR and Sentinel-1 datasets over Los Angeles of Southern California. The result of ASAR first demonstrates the effectiveness and reliability of the AFMTD method by referring to the assumed ground truth of simultaneous MERIS observations. The results of Sentinel-1 data show that over 95% of unwrapped interferograms have the minimum root-mean-square values after AFMTD correction for both descending and ascending tracks. The validation against GPS observation presents that the RMSEs of InSAR displacement time series after AFMTD correction decreases at more than 90% of 125 GPS stations. The average reductions of RMSE are 35.79% and 36.28% for descending and ascending data, respectively, and the maximum improvement is more than 70%. Overall, the proposed AFMTD method outperforms any single model for InSAR tropospheric delay correction and provides an open framework to fuse multi-source tropospheric delay estimates.

Deformation monitoring and risk identification of sea-crossing bridges are essential to mitigate hazards and prevent loss of human life and property. Satellite-based Synthetic Aperture Radar Interferometry (InSAR) technology can detect millimeter-scale deformation, showing unique advantages in the safety monitoring of sea-crossing bridges. However, the existing InSAR methods only extract point-like targets (PTs) based on the coherent index, but ignores the analysis of multiple SAR incoherent information and the foreground-background scattering characteristics differences of bridges, leading to low-density and low-accuracy of PTs on sea-crossing bridges. Moreover, most InSAR-based studies identified structural risks according to deformation measurements without fully considering the various safe deformation ranges of different structural components, resulting in high false-alarm/miss-detection rates in structural risk identification of sea-crossing bridges. To address these issues, a structure knowledge-InSAR integration approach is developed for high-precision deformation monitoring and reliable risk identification of sea-crossing bridges. Firstly, the SAR incoherent information and foreground-background scattering characteristics of the bridge structure are analyzed and applied to improve the density of extractable PTs and remove the incorrect noise signals. Then, the bridge structural mechanics model is combined with the InSAR time-series displacements to analyze the mechanical property degradation of different bridge components, improving the reliability of InSAR-based structural risk identification. This approach is applied to the Stonecutters Bridge and Tsing Ma Bridge using the TerraSAR-X and COSMO-SkyMed images from 2011 to 2012 and the Sentinel-1A images from 2015 to 2017. The results indicate that the densities of PTs extracted on the two bridges increased by about 40% using the new approach, and incorrect noise signals are removed. Moreover, the mechanical properties of different bridge components can be evaluated through the analysis of their structural stress and time-series displacements, helping to decrease the false-alarm/miss-detection rates of InSAR-based structural risk identification. The bridge deformation is correlated with the temperature variation when the temperature difference is large (≥10 °C), but no longer dominated by thermal dilation when the temperature difference is less than 10 °C due to the influence of environmental effects.

As the longest water transfer route in the world, the South-North Water Diversion Project (SNWDP) plays a key role in mitigating the serious water shortage problem in the arid north China, and thus the routine stability monitoring of this mega infrastructure becomes a critical issue to ensure continuous fresh water supply. Compared with traditional geodetic tools such as leveling and GNSS that can only measure deformation at discrete points along the canal, modern satellite Synthetic Aperture Radar Interferometry (InSAR) that can capture subtle ground deformation over wide areas is much more suitable to accomplish this challenging task. In this study, we carried out multi-scale InSAR analyses with multi-track Sentinel-1 data stacks to monitor deformation along the Middle Route canal of SNWDP. A coarse-resolution InSAR analysis was first carried out to map regional land subsidence in north China, and the result suggested that the Middle Route canal bypassed the huge subsidence funnel in the southeast part of Hebei province and exhibited overall stability. Afterwards, the results of relatively high-resolution InSAR analyses performed within the 5 km buffering zones along the entire canal revealed that a few sections were affected by significant embankment deformation or land subsidence. In particular, the high-filled canal sections are more prone to deform, such as the cases in Ye County and Jiaozuo City in Henan province. The validation of InSAR deformation measurements with synchronous leveling in Jiaozuo City indicated that a measurement accuracy of 6 mm was achieved. Another big problem identified is the severe uneven land subsidence along the Tianjin Branch Route, which may pose great threats upon the long-term safety of local water transfer operation, and must be handled properly to avoid further degradation.

The aerial scene classification is one of the major tasks in the remote sensing community that automatically labels the corresponding semantic categories of aerial images. Recently, a lot of methods based on deep neural networks have been proposed, in which hierarchical internal feature are extracted for representations. However, these presented methods often have complex structures and require large volume of memory, and a large number of labeled aerial scene images are difficult to obtain, hindering their implementation in practical applications. In this paper, we present the between-class similarity priori and adaptive knowledge mimic (BPKM) method for aerial scene classification. First, the method extracts the efficient prior relationship information of the scene images from large-scale network. Then, a compressed network is generated through learning the output and the intermediate representations of the large-scale network, and the compressed network achieves better feature description ability; in addition, an improved cross-entropy method with an adaptive threshold is applied to reduce the training time consumption. A largescale data set (AID) and UC-Merced data set are considered for performance evaluation, and the experimental results indicate that the proposed method is about 24× parameters saving compared to popular networks, e.g., AlexNet, and has outstanding classification performance in classes with similar features.

Small-to-moderate earthquakes (e.g. ≤Mw5.5) occur much more frequently than large ones (e.g. >Mw6.0), yet are difficult to study with InSAR due to their weak surface deformation that are severely contaminated by atmospheric delays. Here we propose a stacking method using time-series SAR images that can effectively suppress atmospheric phase screens and extract weak coseismic deformation in centimeter to sub-centimeter level. Using this method, we successfully derive coseismic surface deformations for three small-to-moderate (Mw∼5) earthquakes in Tibet Plateau and Tienshan region from time-series Sentinel-1 SAR images, with peak line-of-sight deformation ranging from 5–6 mm to 13 mm. We also propose a strategy to downsample interferograms with weak deformation signal based on quadtree mesh obtained from preliminary slip model. With the downsampled datasets, we invert for the centroid locations, fault geometries and slips of these events. Our results demonstrate the potential of using time-series InSAR images to enrich earthquake catalog with geodetic observations for further study of earthquake cycle and active tectonics.

Radargrammetry is a useful approach to generate Digital Surface Models (DSMs) and an alternative to InSAR techniques that are subject to temporal or atmospheric decorrelation. Stereo image matching in radargrammetry refers to the process of determining homologous points in two images. The performance of image matching influences the final quality of DSM used for spatial-temporal analysis of landscapes and terrain. In SAR image matching, local matching methods are commonly used but usually produce sparse and inaccurate homologous points adding ambiguity to final products; global or semi-global matching methods are seldom applied even though more accurate and dense homologous points can be yielded. To fill this gap, we propose a hierarchical semi-global matching (SGM) pipeline to reconstruct DSMs in forested and mountainous regions using stereo TerraSAR-X images. In addition, three penalty functions were implemented in the pipeline and evaluated for effectiveness. To make accuracy and efficiency comparisons between our SGM dense matching method and the local matching method, the normalized cross-correlation (NCC) local matching method was also applied to generate DSMs using the same test data. The accuracy of radargrammetric DSMs was validated against an airborne photogrammetric reference DSM and compared with the accuracy of NASA’s 30 m SRTM DEM. The results show the SGM pipeline produces DSMs with height accuracy and computing efficiency that exceeds the SRTM DEM and NCC-derived DSMs. The penalty function adopting the Canny edge detector yields a higher vertical precision than the other two evaluated penalty functions. SGM is a powerful and efficient tool to produce high-quality DSMs using stereo Spaceborne SAR images.

On 17 June 2019, an Ms6.0 earthquake occurred in Changning, Sichuan, China (Changning event), which was the largest earthquake on record within 50 km of the area. It attracted great attention as the area has the largest shale gas production in China as well as significant mineral salt production. Using the Interferometric Synthetic Aperture Radar (InSAR), we extract the coseismic deformation of the Changning event and two earlier Ms > 5.0 earthquakes which occurred in the same region (16 December 2018 Ms5.7 and 3 January 2019 Ms5.3) from the Sentinel-1 and ALOS2 data. We use nonlinear and linear methods to invert the fault models of the three earthquakes based on the deformation fields. The final model shows that the Changning event was caused by a fault with left-lateral strike and thrust slip. The strike is 124.3° with a dip angle of 43.4°. The seismic moment obtained by inversion is 5.28 × 1017 Nm, corresponding to Mw 5.78. Based on the fault models, we analyze the cause of the Changning earthquake considering the local tectonic setting, Coulomb stress change, mining, and fluid injection. We consider that the event may be related to salt mining. The two earlier Ms > 5.0 earthquakes may also play an important role in advancing the Changning earthquake.

This paper reports on the activities carried out in the context of “Dragon project 32278: Three- and Four-Dimensional Topographic Measurement and Validation”. The research work was split into three subprojects and encompassed several activities to deliver accurate characterization of targets on land surfaces and deepen the current knowledge on the exploitation of Synthetic Aperture Radar (SAR) data. The goal of Subproject 1 was to validate topographic mapping accuracy of various ESA, TPM, and Chinese satellite system on test sites in the EU and China; define and improve validation methodologies for topographic mapping; and develop and setup test sites for the validation of different surface motion estimation techniques. Subproject 2 focused on the specific case of spatially and temporally decorrelating targets by using multi-baseline interferometric (InSAR) and tomographic (TomoSAR) SAR processing. Research on InSAR led to the development of robust retrieval techniques to estimate target displacement over time. Research on TomoSAR was focused on testing or defining new processing methods for high-resolution 3D imaging of the interior of forests and glaciers and the characterization of their temporal behavior. Subproject 3 was focused on near-real-time motion estimation, considering efficient algorithms for the digestion of new acquisitions and for changes in problem parameterization.

In this geo-statistical analysis of change detection, we illustrate the evolution of the built-up environment in Shanghai at the street-block level. Based on two TerraSAR-X image stacks with 36 and 15 images, covering the city centre of Shanghai for the time period from 2008 to 2015, a set of coherence images was created using a small baseline approach. The road network from Open Street Map, a volunteered geographic information product, serves as the input dataset to create street-blocks. A street-block is surrounded by roads and resembles a ground parcel, a real estate property – a cadastral unit. The coherence information is aggregated to these street-blocks for each observation and the variation is analysed over time. An analysis of spatial autocorrelation reveals clusters of similar behaviours. The result is a detailed map of Shanghai highlighting areas of change. We argue that the aggregation and grouping of synthetic aperture radar coherence image information to real-world entities (street-blocks) is comprehensible and relevant to the urban planning process. Therefore, this research is a contribution to the community of urban planners, designers, and government agencies who want to monitor the development of the urban landscape.

Here, the authors use the K-Shape algorithm, which is based on a z-normalised version of cross-correlation distance measure, to extract change patterns from time series synthetic aperture radar (SAR) images. These change patterns focus only on the shape of time series, rather than amplitude and phase. Besides, they employ a simple and elegant method to choose the optimal number of clusters. Experimental results on multi-temporal SAR images of Shanghai indicate the effectiveness of their approach.

With the development of interferometric synthetic aperture radar (InSAR), the seismic deformation observation density increases sharply. Data down-sampling can effectively reduce the observation density and the computational cost for subsequent researches. Considering the saliency of the deformation field, we introduce a saliency-based quadtree algorithm for down-sampling (SQS). Three simulation experiments show that SQS can effectively distinguish the near-field and far-field deformation, as well as reduce the amount of observation, while keeping the detailed information of the main deformation near the fault. SQS can avoid the interference of far-field local deformation better than the traditional quadtree sampling algorithm (QS), thus obtaining better inversion results. We took the Dingri earthquake on 20 March 2020 as a case study to verify the advantages of SQS in dealing with real earthquake deformation. We obtained the co-seismic deformation from the ascending and descending Sentinel-1 for the Dingri earthquake, using QS and SQS for sampling and inversion separately. The results show the advantages of SQS in data volume reduction, observation distribution, anti-interference of local deformation, and inversion accuracy. Our preferred solution based on SQS shows that the Dingri earthquake was caused by a normal fault slip. The main slip area is 2–5.5 km deep with a maximum slip of 0.68 m. The estimated geodetic moment is 3.14 × 1017 Nm, corresponding to a magnitude of Mw5.63.

Semantic segmentation is an important field for automatic processing of remote sensing image data. Existing algorithms based on Convolution Neural Network (CNN) have made rapid progress, especially the Fully Convolution Network (FCN). However, problems still exist when directly inputting remote sensing images to FCN because the segmentation result of FCN is not fine enough, and it lacks guidance for prior knowledge. To obtain more accurate segmentation results, this paper introduces edge information as prior knowledge into FCN to revise the segmentation results. Specifically, the Edge-FCN network is proposed in this paper, which uses the edge information detected by Holistically Nested Edge Detection (HED) network to correct the FCN segmentation results. The experiment results on ESAR dataset and GID dataset demonstrate the validity of Edge-FCN.

Synthetic Aperture Radar (SAR) Tomography is a technique to provide direct three-dimensional (3D) imaging of the illuminated targets by processing SAR data acquired from different trajectories. In a large part of the literature, 3D imaging is achieved by assuming mono-dimensional (1D) approaches derived from SAR Interferometry, where a vector of pixels from multiple SAR images is transformed into a new vector of pixels representing the vertical profile of scene reflectivity at a given range, azimuth location. However, mono-dimensional approaches are only suited for data acquired from very closely-spaced trajectories, resulting in coarse vertical resolution. In the case of continuous media, such as forests, snow, ice sheets and glaciers, achieving fine vertical resolution is only possible in the presence of largely-spaced trajectories, which involves significant complications concerning the formation of 3D images. The situation gets even more complicated in the presence of irregular trajectories with variable headings, for which the one theoretically exact approach consists of going back to raw SAR data to resolve the targets by 3D back-projection, resulting in a computational burden beyond the capabilities of standard computers. The first aim of this paper is to provide an exhaustive discussion of the conditions under which high-quality tomographic processing can be carried out by assuming a 1D, 2D, or 3D approach to image formation. The case of 3D processing is then further analyzed, and a new processing method is proposed to produce high-quality imaging while largely reducing the computational burden, and without having to process the original raw data. Furthermore, the new method is shown to be easily parallelized and implemented using GPU processing. The analysis is supported by results from numerical simulations as well as from real airborne data from the ESA campaign AlpTomoSAR.

With the rapid development of artificial intelligence, how to take advantage of deep learning and big data to classify polarimetric synthetic aperture radar (PolSAR) imagery is a hot topic in the field of remote sensing. As a key step for PolSAR image classification, feature extraction technology based on target decomposition is relatively mature, and how to extract discriminative spatial features and integrate these features with polarized information to maximize the classification accuracy is the core issue. In this context, this paper proposes a PolSAR image classification algorithm based on fully convolutional networks (FCNs) and a manifold graph embedding model. First, to describe different types of land objects more comprehensively, various polarized features of PolSAR images are extracted through seven kinds of traditional decomposition methods. Afterwards, drawing on transfer learning, the decomposed features are fed into multiple parallel and pre-trained FCN-8s models to learn deep multi-scale spatial features. Feature maps from the last layer of each FCN model are concatenated to obtain spatial polarization features with high dimensions. Then, a manifold graph embedding model is adopted to seek an effective and compact representation for spatially polarized features in a manifold subspace, simultaneously removing redundant information. Finally, a support vector machine (SVM) is selected as the classifier for pixel-level classification in a manifold subspace. Extensive experiments on three PolSAR datasets demonstrate that the proposed algorithm achieves a superior classification performance.

Synthetic Aperture Rradar (SAR) provides rich ground information for remote sensing survey and can be used all time and in all weather conditions. Polarimetric SAR (PolSAR) can further reveal surface scattering difference and improve radar’s application ability. Most existing classification methods for PolSAR imagery are based on manual features, such methods with fixed pattern has poor data adaptability and low feature utilization, if directly input to the classifier. Therefore, combining PolSAR data characteristics and deep network with auto-feature learning ability forms a new breakthrough direction. In fact, feature learning of deep network is to realize function approximation from data to label, through multi-layer accumulation, but finite layers limit the network’s mapping ability. According to manifold hypothesis, high-dimensional data exists in potential low-dimensional manifold and different types of data locates in different manifolds. Manifold learning can model core variables of the target, and separate different data’s manifold as much as possible, so as to complete data classification better. Therefore, taking manifold hypothesis as a starting point, nonlinear manifold learning integrated with fully convolutional networks for PolSAR image classification method is proposed in this paper. Firstly, high-dimensional polarized features are extracted based on scattering matrix and coherence matrix of original PolSAR data, whose compact representation is mined by manifold learning. Meanwhile, drawing on transfer learning, pre-trained Fully Convolutional Networks (FCN) model is utilized to learn deep spatial features of PolSAR imagery. Considering complementary advantages, weighted strategy is adopted to embed manifold representation into deep spatial features, which are input into support vector machine (SVM) classifier for final classification. A series of experiments on three PolSAR datasets have verified effectiveness and superiority of the proposed classification algorithm.

Due to the development of deep convolutional neural networks (CNNs), great progress has been made in semantic segmentation recently. In this paper, we present an end-to-end Bayesian segmentation network based on generative adversarial networks (GANs) for remote sensing images. First, fully convolutional networks (FCNs) and GANs are utilized to realize the derivation of the prior probability and the likelihood to the posterior probability in Bayesian theory. Second, the cross-entropy loss in the FCN serves as an a priori to guide the training of the GAN, so as to avoid the problem of mode collapse during the training process. Third, the generator of the GAN is used as a teachable spatial filter to construct the spatial relationship between each label. Some experiments were performed on two remote sensing datasets, and the results demonstrate that the training of the proposed method is more stable than other GAN based models. The average accuracy and mean intersection (MIoU) of the two datasets were 0.0465 and 0.0821, and 0.0772 and 0.1708 higher than FCN, respectively.

A catastrophic landslide happened on 15 March 2019 in Xiangning County of Shanxi Province, causing 20 fatalities. Such an event makes us realize the significance of loess slope instability detection. Therefore, it is essential to identify the potential active landslides, monitor their displacements, and sort out dominant controlling factors. Synthetic Aperture Radar (SAR) Interferometry (InSAR) has been recognized as an effective tool for geological hazard mapping with wide coverage and high precision. In this study, the time series InSAR analysis method was applied to map the unstable areas in Xiangning County, as well as surrounding areas from C-band Sentinel-1 datasets acquired from March 2017 to 2019. A total number of 597 unstable sites covering 41.7 km2 were identified, among which approximately 70% are located in the mountainous areas which are prone to landslides. In particular, the freezing and thawing cycles might be the primary triggering factor for the failure of the Xiangning landslide. Furthermore, the nonlinear displacements of the active loess slopes within this region were found to be correlated significantly with precipitation. Therefore, a climate-driven displacement model was employed to explore the quantitative relationship between rainfall and nonlinear displacements.

The classification of synthetic aperture radar (SAR) images is of great importance for rapid scene understanding. Recently, convolutional neural networks (CNNs) have been applied to the classification of single-polarized SAR images. However, it is still difficult due to the random and complex spatial patterns lying in SAR images, especially in the case of finite training data. In this paper, a pattern statistics network (PSNet) is proposed to address this problem. PSNet borrows the idea from the statistics and probability theory and explicitly embeds the random nature of SAR images in the representation learning. In the PSNet, both fluctuation and pattern representations are extracted for SAR images. More specifically, the fluctuation representation does not consider the rigorous relationships between local pixels and only describes the average fluctuation of local pixels. By contrast, the pattern representation is devoted to hierarchically capturing the interactions between local pixels, namely, the spatial patterns of SAR images. The proposed PSNet is evaluated on three real SAR data, including spaceborne and airborne data. The experimental results indicate that the fluctuation representation is useful and PSNet achieves superior performance in comparison with related CNN-based and texture-based methods.

Semantic segmentation is an important process of scene recognition with deep learning frameworks achieving state of the art results, thus gaining much attention from the remote sensing community. In this paper, an end-to-end conditional random fields generative adversarial segmentation network is proposed. Three key factors of this algorithm are as follows. First, the network combines generative adversarial network and Bayesian framework to realize the estimation from the prior probability to the posterior probability. Second, the skip connected encoder-decoder network is combined with CRF layer to implement end-to-end network training. Finally, the adversarial loss and the cross-entropy loss guide the training of the segmentation network through back propagation. The experimental results show that our proposed method outperformed FCN in terms of mIoU for 0.0342 and 0.11 on two data sets, respectively.

The convolutional neural network (CNN) has shown great potential in many fields; however, transferring this potential to synthetic aperture radar (SAR) image interpretation is still a challenging task. The coherent imaging mechanism causes the SAR signal to present strong fluctuations, and this randomness property calls for many degrees of freedom (DoFs) for the SAR image description. In this paper, a statistics learning network (SLN) based on the quadratic form is presented. The statistical features are expected to be fitted in the SLN for SAR image representation. (i) Relying on the quadratic form in linear algebra theory, a quadratic primitive is developed to comprehensively learn the elementary statistical features. This primitive is an extension to the convolutional primitive that involves both nonlinear and linear transformations and provides more flexibility in feature extraction. (ii) With the aid of this quadratic primitive, the SLN is proposed for the classification task. In the SLN, different types of statistics of SAR images are automatically extracted for representation. Experimental results on three datasets show that the SLN outperforms a standard CNN and traditional texture-based methods and has potential for SAR image classification.

Thanks to the availability of large-scale data, deep Convolutional Neural Networks (CNNs) have witnessed success in various applications of computer vision. However, the performance of CNNs on Synthetic Aperture Radar (SAR) image classification is unsatisfactory due to the lack of well-labeled SAR data, as well as the differences in imaging mechanisms between SAR images and optical images. Therefore, this paper addresses the problem of SAR image classification by employing the Generative Adversarial Network (GAN) to produce more labeled SAR data. We propose special GANs for generating SAR images to be used in the training process. First, we incorporate the quadratic operation into the GAN, extending the convolution to make the discriminator better represent the SAR data; second, the statistical characteristics of SAR images are integrated into the GAN to make its value function more reasonable; finally, two types of parallel connected GANs are designed, one of which we call PWGAN, combining the Deep Convolutional GAN (DCGAN) and Wasserstein GAN with Gradient Penalty (WGAN-GP) together in the structure, and the other, which we call CNN-PGAN, applying a pre-trained CNN as a discriminator to the parallel GAN. Both PWGAN and CNN-PGAN consist of a number of discriminators and generators according to the number of target categories. Experimental results on the TerraSAR-X single polarization dataset demonstrate the effectiveness of the proposed method.

Persistent Scatterer Interferometry (PS-InSAR) has become an indispensable tool for monitoring surface motion in urban environments. The interferometric configuration of PS-InSAR tends to mix topographic and deformation components in differential interferometric observations. When the upcoming constellation missions such as, e.g., TanDEM-L or TWIN-L provide new standard operating modes, bi-static stacks for deformation monitoring will be more commonly available in the near future. In this paper, we present an analysis of the applicability of such data sets for urban monitoring, using a stack of pursuit monostatic data obtained during the scientific testing phase of the TanDEM-X (TDX) mission. These stacks are characterized by extremely short temporal baselines between the TerraSAR-X (TSX) and TanDEM-X acquisitions at the same interval. We evaluate the advantages of this acquisition mode for urban deformation monitoring with several of the available acquisition pairs. Our proposed method exploits the special properties of this data using a modified processing chain based on the standard PS-InSAR deformation monitoring procedure. We test our approach with a TSX/TDX mono-static pursuit stack over Guangzhou, using both the proposed method and the standard deformation monitoring procedure, and compare the two results. The performance of topographic and deformation estimation is improved by using the proposed processing method, especially regarding high-rise buildings, given the quantitative statistic on temporal coherence, detectable numbers, as well as the PS point density of persistent scatters points, among which the persistent scatter numbers increased by 107.2% and the detectable height span increased by 78% over the standard processing results.

In the areas with steep topography and vulnerable geological condition, landslide deformation monitoring is an important task for risk assessment and management. Differential Synthetic-Aperture Radar interferometry (D-InSAR) and Persistent Scatterer Interferometry (PS-InSAR) are two advanced SAR Interferometry techniques for detection, analysis and monitoring of slow moving landslides. The techniques can be used to identify wide displacement areas and measure displacement rates over long time series with millimeter-level accuracy. In this paper, multiple SAR datasets of Advanced Land Observing Satellite (ALOS) Phased Array L-band Synthetic Aperture Radar (PALSAR) and Environmental Satellite (ENVISAT) C-band Advanced Synthetic Aperture Radar (ASAR) are used for landslide monitoring with both D-InSAR and PS-InSAR techniques in Badong at the Three Gorges area in China. Two areas of significant deformation along the southern riverbank of Yangtze River in Badong are identified by joint analyses of PS-InSAR results from different data stacks. Furthermore, both qualitative and quantitative evaluations of the PS-InSAR results are carried out together with preliminary correlation analysis between the time series deformation of a PS point in high risk location and the temporal variation of water level in the Three Gorges Reservoir.

Automatic image registration of optical-to-Synthetic aperture radar (SAR) images is difficult because of the inconsistency of radiometric and geometric properties between the optical image and the SAR image. The intensity-based methods may require many calculations and be ineffective when there are geometric distortions between these two images. The feature-based methods have high requirements on features, and there are certain challenges in feature extraction and matching. A new automatic optical-to-SAR image registration framework is proposed in this paper. First, modified holistically nested edge detection is employed to detect the main contours in both the optical and SAR images. Second, a mesh grid strategy is presented to perform a coarse-to-fine registration. The coarse registration calculates the feature matching and summarizes the preliminary results for the fine registration process. Finally, moving direct linear transformation is introduced to perform a homography warp to alleviate parallax. The experimental results show the effectiveness and accuracy of our proposed method.

Excessive groundwater exploitation is common through the Taiyuan basin, China, and is well known to result in ground subsidence. However, most ground subsidence studies in this region focus on a single place (Taiyuan city), and thus fail to demonstrate the regional extent of the deformation phenomena in the whole basin. In this study, we used Interferometric Synthetic Aperture Radar (InSAR) time series analysis to investigate land subsidence across the entire Taiyuan basin region. Our data set includes a total of 75 ENVISAT ASAR images from two different frames acquired from August 2003 to September 2010 and 33 TerraSAR-X scenes spanning between March 2009 and March 2010. ERA-Interim reanalysis was used to correct the stratified delay to reduce the bias expected from the systematic components of tropospheric delay. The residual delay after correction of stratified delay can be considered as a stochastic component and be mitigated through spatial-temporal filtering. A comparison with MERIS (Medium-Resolution Imaging Spectrometer) measurements indicates that our atmospheric corrections improved agreement over the conventional spatial-temporal filtering by about 20%. The displacement results from our atmosphere-corrected time series InSAR were further validated with continuous GPS data. We found eight subsiding centers in the basin and a surface uplift to the north of Taiyuan city. The causes of ground deformation are analyzed and discussed in relation to gravity data, pre-existing faults, and types of land use.

Interferometric synthetic aperture radar (InSAR) is an effective technology for generating high-precision digital elevation models (DEMs). However, the vertical accuracy of InSAR DEMs is limited by the contradiction between height measurement sensitivity and phase unwrapping reliability in terms of normal baseline length as well as data voids caused by layover or shadow effects. In order to alleviate these two unfavorable factors, in this study, a novel InSAR DEM fusion method with guided filter is developed and assessed with multiple bistatic TanDEM-X InSAR data pairs of different normal baselines acquired from different orbits. Unlike the widely used fusion method based on pixel-by-pixel weighted average, the guided-filter-based method incorporates local spatial context information into the fusion and can thus effectively alleviate the noise effect and automatically fill in data voids. As a result of the local edge-preserving capability of the guided filter, the proposed fusion method can preserve terrain details by maintaining gradient consistency and introducing terrain features as guidance image. Furthermore, the proposed fusion method is computationally efficient owing to the linear time complexity of guided filter. The experimental results show that the fused DEM with guided filter can depict terrain details well and smooth the “salt-and-pepper” noise and fill in almost all of the data voids. The root mean square error (RMSE) of the fused InSAR DEM with guided filter is lower than those of the weighted average fused InSAR DEM and the TanDEM-X DEM released by the German Aerospace Center (DLR), thus validating the effectiveness of the fusion method proposed in this study.

Temperature variation plays a significant role in the long-term structural behaviour of civil infrastructures, but very few quantitative studies have measured and analysed the infrastructures’ global thermal dilation because of their large sizes and geometric complexities. The modern Differential Synthetic Aperture Radar Interferometry (DInSAR) technique has great potential in applications of their thermal dilation mapping and characterization due to the techniques’ unique capabilities for use in large areas, with high-resolution, and at low-costs for deformation measurements. However, the practical application of DInSAR in thermal dilation estimation is limited by difficulty in the precise separation from the residual topographic phase and the trend deformation phase. Moreover, due to a lack of thermal dilation characteristics analyses in previous studies, the thermal dilation mechanisms are still unclear to users, which restricts the accurate understanding of the thermal dilation evolution process. Given the above challenges, an advanced multi-temporal DInSAR approach is proposed in this study, and the effectiveness of this approach was presented using three cases studies concerning different infrastructure types. In this method, the coherent, incoherent, and semantic information of structures were combined in order to refine the detection of point-like targets (PTs). Interferometric subsets with small temporal baselines and temperature differences were used for high-resolution topography estimation. A pre-analysis was adopted to determine the transmission direction, spatial pattern, and temporal variation of the thermal dilation. Then, both the traditional least squares estimation and our robust coherence-weighted least squares regression analysis were performed between the time series displacements and the corresponding temperatures to quantitatively estimate the thermal dilation model. The results were verified in terms of the estimated linear thermal dilation coefficient, which indicates the greater reliability of our method. Furthermore, the thermal dilation characteristics of different civil infrastructure types were analysed, which facilitates a greater understanding of the thermal dilation evolution process of civil infrastructures.

In this paper, a component-based multi-layer parallel network is proposed for airplane detection in Synthetic Aperture Radar (SAR) imagery. In response to the problems called sparsity and diversity brought by SAR scattering mechanism, depth characteristics and component structure are utilized in the presented algorithm. Compared with traditional features, the depth characteristics have better description ability to deal with diversity. Component information is contributing in detecting complete targets. The proposed algorithm consists of two parallel networks and a constraint layer. First, the component information is introduced into the network by labeling. Then, the overall target and corresponding components are detected by the trained model. In the following discriminative constraint layer, the maximum probability and prior information are adopted to filter out wrong detection. Experiments for several comparative methods are conducted on TerraSAR-X SAR imagery; the results indicate that the proposed network has a higher accuracy for airplane detection.

For InSAR topographic mapping, multi-baseline InSAR height estimation is known to be an effective way to facilitate phase unwrapping by significantly increasing the ambiguity intervals and maintaining good height measurement sensitivity, especially in mountainous areas. In this paper, an efficient multi-baseline SAR interferometry approach based on maximum likelihood estimation is developed for refined topographic mapping in mountainous areas. In the algorithm, maximum likelihood (ML) height estimation is used to measure the topographic details and avoid the complicated phase unwrapping process. In order to be well-adapted to the mountainous terrain conditions, the prior height probability is re-defined to take the local terrain conditions and neighboring height constraint into consideration in the algorithm. In addition, three strategies are used to optimize the maximum likelihood height estimation process to obtain higher computational efficiency, so that this method is more suitable for spaceborne InSAR data. The strategies include substituting a rational function model into the complicated conversion process from candidate height to interferometric phase, discretizing the continuous height likelihood probability, and searching for the maximum likelihood height with a flexible step length. The experiment with simulated data is designed to verify the improvement of the ML height estimation accuracy with the re-defined prior height distribution. Then the optimized processing procedure is tested with the multi-baseline L-band ALOS/PALSAR data covering the Mount Tai area in China. The height accuracy of the generated multi-baseline InSAR DEM can meet both standards of American DTED-2 and Chinese national 1:50,000 DEM (mountain) Level 2.

Inspired by enormous success of fully convolutional network (FCN) in semantic segmentation, as well as the similarity between semantic segmentation and pixel-by-pixel polarimetric synthetic aperture radar (PolSAR) image classification, exploring how to effectively combine the unique polarimetric properties with FCN is a promising attempt at PolSAR image classification. Moreover, recent research shows that sparse and low-rank representations can convey valuable information for classification purposes. Therefore, this paper presents an effective PolSAR image classification scheme, which integrates deep spatial patterns learned automatically by FCN with sparse and low-rank subspace features: (1) a shallow subspace learning based on sparse and low-rank graph embedding is firstly introduced to capture the local and global structures of high-dimensional polarimetric data; (2) a pre-trained deep FCN-8s model is transferred to extract the nonlinear deep multi-scale spatial information of PolSAR image; and (3) the shallow sparse and low-rank subspace features are integrated to boost the discrimination of deep spatial features. Then, the integrated hierarchical subspace features are used for subsequent classification combined with a discriminative model. Extensive experiments on three pieces of real PolSAR data indicate that the proposed method can achieve competitive performance, particularly in the case where the available training samples are limited.

Large-scale reclamation projects during the past decades have been recognized as one of the driving factors behind land subsidence in coastal areas. However, the pattern of temporal evolution in reclamation settlements has rarely been analyzed. In this work, we study the spatio-temporal evolution pattern of Linggang New City (LNC) in Shanghai, China, using space-borne synthetic aperture radar interferometry (InSAR) methods. Three data stacks including 11 X-band TerraSAR-X, 20 L-band ALOS PALSAR, and 35 C-band ENVISAT ASAR images were used to retrieve time series deformation from 2007 to 2010 in the LNC. An InSAR analysis from the three data stacks displays strong agreement in mean deformation rates, with coefficients of determination of about 0.9 and standard deviations for inter-stack differences of less than 4 mm/y. Meanwhile, validations with leveling data indicate that all the three data stacks achieved millimeter-level accuracies. The spatial distribution and temporal evolution of deformation in the LNC as indicated by these InSAR analysis results relates to historical reclamation activities, geological features, and soil mechanisms. This research shows that ground deformation in the LNC after reclamation projects experienced three distinct phases: primary consolidation, a slight rebound, and plateau periods.

This paper proposes an innovative Adaptive Component Selection-Based Discriminative Model (ACSDM) for object detection in high-resolution synthetic aperture radar (SAR) imagery. In order to explore the structural relationships between the target and the components, a multi-scale detector consisting of a root filter and several part filters is established, using Histogram of Oriented Gradient (HOG) features to describe the object from different resolutions. To make the detected components of practical significance, the size and anchor position of each component are determined through statistical methods. When training the root model and the corresponding part models, manual annotation is adopted to label the target in the training set. Besides, a penalty factor is introduced to compensate information loss in preprocessing. In the detection stage, the Small Area-Based Non-Maximum Suppression (SANMS) method is utilised for filtering out duplicate results. In the experiments, the aeroplanes in TerraSAR-X SAR images are detected by the ACSDM algorithm and different comparative methods. The results indicate that the proposed method has a lower false alarm rate and can detect the components accurately.