Civil infrastructural safety is a key indicator for a sustainable built environment. Urban ground subsidence and infrastructural deformation are threatening infrastructural health and human well-being, in particular, in those rapidly urbanized regions in China and other Asian developing countries. Today, advances in satellite and terrestrial sensor technologies are enabling the creation of an integrated satellite–terrestrial sensor network for regular 'Urban Computed Tomography Diagnosis' and early warning of potential deformation disasters. This paper reviews the key earth observation and terrestrial sensor technologies for urban infrastructural health diagnosis, and proposes the potentials and prospects for future development of the satellite–terrestrial system.
The Lianjiang Plain in China and ancient villages distributed within the plain are under the potential threat of surface motion change, but no effective monitoring strategy currently exists. Distributed Scatterer InSAR (DSInSAR) provides a new high-resolution method for the precise detection of surface motion change. In contrast to the first-generation of time-series InSAR methodology, the distributed scatterer-based method focuses both on pointwise targets with high phase stability and distributed targets with moderate coherence, the latter of which is more suitable for the comprehensive environment of the Lianjiang Plain. In this paper, we present the first study of surface motion change detection in the Lianjiang Plain, China. Two data stacks, including 54 and 29 images from Sentinel-1A adjacent orbits, are used to retrieve time-series surface motion changes for the Lianjiang Plain from 2015 to 2018. The consistency of measurement has been cross-validated between adjacent orbit results with a statistically significant determination coefficient of 0.92. The temporal evolution of representative measuring points indicates three subzones with varied surface patterns: Eastern Puning (Zone A) in a slight elastic rebound phase with a moderate deformation rate (0–40 mm/year), Chaonan (Zone B) in a substantial subsidence phase with a strong deformation rate (−140–0 mm/year), and Chaoyang (Zone C) in a homogeneous and stable situation (−10–10 mm/year). The spatial distribution of these zones suggests a combined change dynamic and a strong concordance of factors impacting surface motion change. Human activities, especially groundwater exploitation, dominate the subsidence pattern, and natural conditions act as a supplementary inducement by providing a hazard-prone environment. The qualitative and quantitative analysis of spatial and temporal details in this study provides a basis for systematic surface motion monitoring, cultural heritage protection and groundwater resources management.
Topographic phase simulation is important for deformation estimation in differential synthetic aperture radar (SAR) interferometry (DInSAR). The most commonly used 30-m resolution SRTM digital elevation model (DEM) is usually required to be resampled due to its relatively low resolution (LR) comparing to the high resolution (HR) SAR images. Although the WorldDEMTM with a 12-m resolution achieves global coverage, it is not available freely. Consequently, it is useful to evaluate the practicability of the super-resolution (SR) from LR SRTM DEMs to HR WorldDEMTM ones, which has not been investigated. Most existing DEM SR models are trained with synthetic datasets in which the LR DEMs are downsampled from their HR counterparts. However, these models become less effective when applied to real-world scenarios due to the domain gap between the synthetic and real LR DEMs. In this paper, we constructed a real-world DEM SR dataset where the LR and HR DEMs were collected from SRTM and WorldDEMTM, respectively. An ESRGAN model was adapted to train on the dataset. Considering that the real LR-HR pairs may suffer from misalignment, we introduced the perceptual loss for better optimizing the model. Moreover, a logarithmic normalization was proposed to compress the wide elevation range and adjust the uneven distribution. We also pretrained the model using natural images since collecting sufficient HR DEMs is costly. Experiments demonstrate that the proposed method achieves near 0.69dB improvement of peak signal-to-noise ratio (PSNR). In addition, our method is also validated to improve the topographic phase simulation by 23.42% of MSE.
Continuous and large-scale surface deformation monitoring is critical for the comprehension of natural hazards and environmental changes. This can be facilitated by time-series Interferometric Synthetic Aperture Radar (TS-InSAR), which provides unprecedented spatial and temporal resolution. However, the original TS-InSAR measurements, being a superposition of trend, seasonal, and noise signals, often suffer from outlier and annual seasonal variations due to the influences of atmospheric delay, especially in coastal and mountainous areas, resulting in skewed monitoring if neglected. To address these issues, an integration method of variational mode decomposition and gated recurrent unit (VMD-GRU) is proposed in this study to enhance the robustness of continuous large-scale surface deformation monitoring. The VMD decomposes low-frequency trend, specific-frequency seasonal, and high-frequency noise components from the original TS-InSAR data via frequency-domain variational optimization first. Then, by eliminating the seasonal component decomposed by VMD from the original time series, the time series is reconstructed, effectively removing the influence of annual seasonal variations. Subsequently, GRU is utilized to further eradicate noise from the reconstructed time series, mitigating the influence of outliers and noise, thereby yielding a trend component that intuitively reflects surface deformation. Experiments on physical-based synthetic and real-world datasets demonstrate that the proposed VMD-GRU outperforms the existing methods. By introducing the frequency priors, the proposed method significantly enhances the robustness and accuracy of continuous large-scale surface deformation monitoring, providing a more reliable understanding of natural hazards and environmental changes.
Land subsidence has been a significant problem in land reclaimed from the sea, and it is usually characterized by a differential settlement pattern due to locally unconsolidated marine sediments and fill materials. Time series Synthetic Aperture Radar Interferometry (InSAR) techniques based on distributed scatterers (DS), which can identify sufficient measurement points (MPs) when point-wise radar targets are lacking, have great potential to measure such differential reclamation settlement. However, the computational time cost has been the main drawback of current distributed scatterer interferometry (DSI) for its applications compared to the standard PSI analysis. In this paper, we adopted an improved DSI processing strategy for a fast and robust analysis of land subsidence in reclaimed regions, which is characterized by an integration of fast statistically homogeneous pixel selection based (FaSHPS-based) DS detection and eigendecomposition phase optimization. We demonstrate the advantages of the proposed DSI strategy in computational efficiency and deformation estimation reliability by applying it to two TerraSAR-X image data stacks from 2008 to 2009 to retrieve land subsidence over two typical reclaimed regions of Hong Kong International Airport (HKIA) and Hong Kong Science Park (HKSP). Compared with the state-of-the-art DSI methods, the proposed strategy significantly improves the computational efficiency, which is enhanced approximately 30 times in DS identification and 20 times in phase optimization. On average, the DSI strategy results in 7.8 and 3.7 times the detected number of MPs for HKIA and HKSP with respect to persistent scatter interferometry (PSI), which enables a very detailed characterization of locally differential settlement patterns. Moreover, the DSI-derived results agree well with the levelling survey measurements at HKIA, with a mean difference of 1.87 mm/yr and a standard deviation of 2.08 mm/yr. The results demonstrate that the proposed DSI strategy is effective at improving target density, accuracy and efficiency in monitoring ground deformation, particularly over reclaimed coastal areas.
Significant progress has occurred in Interferometric Synthetic Aperture Radar (InSAR), emerging as a crucial technique for monitoring surface deformation. This evolution is attributed to expanded Synthetic Aperture Radar (SAR) data availability and improved data quality. However, effectively managing and processing SAR big data presents substantial challenges for algorithms and pipelines, especially in large-scale contexts. In this paper, we introduce a parallel time-series InSAR processing platform that leverages High-Performance Computing (HPC) clusters for efficiently managing and processing large-scale SAR data, and incorporates Graphics Processing Unit (GPU) acceleration to significantly enhance the speed and efficiency of specific InSAR processing algorithms. Our approach encompasses high-quality data compression, integration of classic InSAR models, and the introduction of a robust Distributed Scatterer InSAR method for time-series processing. The platform efficiently handles massive data, featuring a parallel optimization tool for acceleration. Additionally, it provides web-based 2D result visualization and 3D outcome representation for comprehensive user understanding. To illustrate our platform's capabilities, we applied it to 40 Sentinel-1 SAR data scenes from Tibet (2017-2019). Our data compression technique notably reduces data size, reducing mask data by 87.5% and coherence data to 25% of its original size. Leveraging HPC and GPU, we achieved a 50% reduction in registration computation time. This study offers valuable insights and a comprehensive platform for InSAR practitioners, facilitating calculations and enhancing comprehension of surface deformation processes. Our system's improved processing efficiency, coupled with a variety of InSAR methods, makes it an alternative choice for InSAR data handling and analysis.
The multispectral instrument (MSI) carried by Sentinel-2A has 13 spectral bands with various spatial resolutions (i.e., four 10-m, six 20-m, and three 60-m bands). A wide range of applications requires a 10-m resolution for all spectral bands, including the 20- and 60-m bands. To achieve this requirement, previous studies used conventional pansharpening techniques, which require a simulated 10-m panchromatic (PAN) band from four 10-m bands [blue, green, red, and near infrared (NIR)]. The simulated PAN band may not have all the information from the original four bands and may have no spectral response function that overlaps the 20- or 60-m bands to be sharpened, which may degrade fusion quality. This paper presents a machine learning method that can directly use the information from multiple 10-m resolution bands for fusion. The method first learns the spectral relationship between the 20- or 60-m band to be sharpened and the selected 10-m bands degraded to 20 or 60 m using the support vector regression (SVR) model. The model is then applied to the selected 10-m bands to predict the 10-m-resolution version of the 20- or 60-m band. The image degradation process was tuned to closely match the Sentinel-2A MSI modulation transfer function (MTF). We applied our method to three data sets in Guangzhou, China, New South Wales, Australia, and St. Louis, USA, and achieved better fusion results than other commonly used pansharpening methods in terms of both visual and quantitative factors.
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