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Three-dimensional SAR imaging of urban buildings is currently a hotspot in the research area of remote sensing. Synthetic Aperture Radar (SAR) offers all-time, all-weather, high-resolution capacity, and is an important tool for the monitoring of building health. Buildings have geometric distortion in conventional 2D SAR images, which brings great difficulties to the interpretation of SAR images. This paper proposes a novel Rotating SAR (RSAR) mode, which acquires 3D information of buildings from two different angles in a single rotation. This new RSAR mode takes the center of a straight track as its rotation center, and obtains images of the same facade of a building from two different angles. By utilizing the differences in geometric distortion of buildings in the image pair, the 3D structure of the building is reconstructed. Compared to the existing tomographic SAR or circular SAR, this method does not require multiple flights in different elevations or observations from varying aspect angles, and greatly simplifies data acquisition. Furthermore, both simulation analysis and actual data experiment have verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

When Synthetic Aperture Radar (SAR) observes complex structural targets such as oil tanks, it is easily interfered with by multipath signals, resulting in a large number of multipath ghost images in the SAR image, which seriously affect the image clarity. To address this problem, this paper proposes a multi-aspect multipath suppression method. This method observes complex structural targets from different azimuth angles to obtain a multi-aspect image sequence and then uses the difference in sequence features between the target image and the multipath ghost image with respect to aspect angle to separate them. This paper takes a floating-roof oil tank as an example to analyze the propagation path and the ghost image characteristics of multipath signals under different observation aspects. We conclude that the scattering center of the multipath ghost image changes with the radar observation aspect, whereas the scattering center of the target image does not. This paper uses the Robust Principal Component Analysis (RPCA) method to decompose the image sequence matrix into two parts: a sparse matrix and a low-rank matrix. The low-rank matrix represents the aspect-stable principal component in the image sequence; that is, the real scattering center. The sparse matrix represents the part of the image sequence that deviates from the principal component; that is, the signal that varies with aspect, mainly including multipath signals, sidelobes, anisotropic signals, etc. By reconstructing the low-rank matrix and the sparse matrix, respectively, we can obtain the image after multipath signal suppression and also the multipath ghost image. Both the target and the multipath signal provide useful information. The image after multipath signal suppression is useful for obtaining the structural information of the target, and the multipath ghost image is useful for analyzing the multipath phenomenon of the complex structure target. This paper conducts experimental verification using real airborne SAR data of an external floating roof oil tank and compares three methods: RPCA, PCA, and sub-aperture fusion method. The experiment shows that the RPCA method can better separate the target image and the multipath ghost image. [ABSTRACT FROM AUTHOR]

Synthetic aperture radar (SAR) is a powerful tool for detecting and imaging targets in enclosed environments, such as tunnels and underground garages. However, SAR performance is degraded by multipath effects, which occur when electromagnetic waves are reflected by obstacles, such as walls, and interfere with the direct signal. This results in the formation of multipath ghost images, which obscure the true target and reduce the image quality. To overcome this challenge, we propose a novel method based on multi-angle observation. This method exploits the fact that the position of ghost images changes depending on the angle of the radar, while the position of the true target remains stable. By collecting and processing multiple data sets from different angles, we can eliminate the ghost images and enhance the target image. In addition, we introduce a center vector distance algorithm to address the complexity and computational intensity of existing multipath suppression algorithms. This algorithm, which defines the primary direction of multi-angle vectors from stable scattering centers as the center vector, processes and synthesizes multiple data sets from multi-angle observations. It calculates the distance of pixel intensity sequences in the composite data image from the center vector. Pixels within a specified threshold are used for imaging, and the final result is obtained. Simulation experiments and real SAR data from underground garages confirm the effectiveness of this method in suppressing multipath ghost images. [ABSTRACT FROM AUTHOR]

Synthetic Aperture Radar (SAR) image change detection aims to detect changes with images of the same area acquired at different times. It has wide applications in environmental monitoring, urban planning and resource management. Traditional change detection methods for spaceborne SAR time-series images typically adopt a pairwise comparison strategy to obtain multi-temporal change information. However, this kind of method has the problem of losing the overall change information, which is time consuming. To address this problem, this paper proposes a new change detection algorithm for spaceborne SAR time-series data based on SAR-SIFT-Logarithm Background Subtraction. This algorithm combines the SAR-SIFT image registration technology with Logarithm Background Subtraction. The method first preprocesses the input time-series data with steps like noise reducing and radiometric calibration. Then, the images will be coregistered by the SAR-SIFT step to avoid mismatches-induced detection performance degradation. Next, the parts that remained unchanged throughout the time period are modeled with a median filter to obtain the static background. The change information is then obtained via the subtraction of background and CFAR detection and clustering. The proposed algorithm is validated using the Sentinel-1 GRD and PAZ-1 time-series dataset. Experimental results demonstrate that the proposed method effectively detects the overall change information and reduces processing time compared to traditional pairwise comparison methods. [ABSTRACT FROM AUTHOR]

In recent years, noticeable subsidence depressions have occurred along the coastal zone of the Yellow River Delta. In some coastal areas, the average annual subsidence varies from tens of millimeters to hundreds of millimeters. Although some studies have discovered a significant land subsidence funnel in the coastal zone of the Yellow River Delta, it has rarely been reported in recent years. Using Sentinel-1A/1B images from the last five years and permanent scatterers interferometric synthetic aperture radar technology, we found a typical subsidence bowl in the northeastern part of the delta, with a cumulative settlement of nearly 1 m over five years. In addition, we used the approach of soil mechanics to simulate the settlement of confined brine layers with different thicknesses under drainage conditions. We found that the 15 m thick confined aquifer can produce 1 m of settlement for every 15% decrease in water content. The simulation results explain the large settlement of the brine industrial area. This study can provide guidance for brine mining in the future. If we continue to overexploit underground brine, there will be more severe land subsidence in the delta in the future. [ABSTRACT FROM AUTHOR]

Global sea level rise is a major environmental concern for many countries and cities, particularly for low-lying coastal areas where urban development is threatened by the combined effects of sea level rise and land subsidence. This study employed an improved two-layer network Persistent Scatterers Interferometric Synthetic Aperture Radar (PS-InSAR) technology to obtain high-precision land subsidence in Singapore from 2015 to 2019. Landsat images from 1973 to 2020 were also utilized to extract changes in Singapore's coastline. Geological, topographical, and global sea level rise data were integrated to investigate the causes and impacts of land subsidence in Singapore. The results indicate that the areas with severe subsidence coincide with land reclamation areas, where subsidence is mainly due to soil consolidation. Based on WorldDEM, land subsidence, and sea level rise data, the maximum inundation depth in Singapore by 2050 is estimated to be 1.24 m, with the Marina Bay area in Singapore's central business district being the most vulnerable to sea level rise. This study provides data support and a scientific basis for understanding the impact of land subsidence on Singapore's coastal areas under the influence of multiple factors using advanced InSAR technology. [ABSTRACT FROM AUTHOR]

Beijing is a major city suffering from land subsidence due to long-term over-exploitation of groundwater. The South-to-North Water Diversion Project (SNWDP), however, has had a significant impact on the structure of water consumption since the end of 2014, and it is changing the status of land subsidence in Beijing. In this study, we employed Persistent Scatterers Synthetic Aperture Radar Interferometric (PS-InSAR) to investigate the decadal evolution of land subsidence in Beijing with 100 TerraSAR-X stripmap images collected from April 2010 to December 2019. The water resources, historic climate and urban construction data were compiled for the years of 2010 to 2019 to analyze changes in groundwater level, human activity, surface geology, active faults and land subsidence patterns. The results show that the changes in the water supply structure are correlated to a rise in groundwater level after 2015. These changes include an increase in the water supply from the SNWDP, a reduction in groundwater exploitation, the optimization of water consumption, replacing recycled water for environmental water and a reduction in the use of water for agriculture. Land subsidence in the study area was concentrated in the eastern regions, trending towards a decreasing velocity starting about two years after the commencement of SNWDP in 2015. Uneven subsidence in the land subsidence area was related to excavations of underground soil, and the construction of Line 6 and Line 7 led to rapid nonlinear subsidence. Our results have scientific significance for reducing subsidence hazards in the context of SNWDP and urban expansion. [ABSTRACT FROM AUTHOR]

Due to many factors in the physical properties of the ground surface, the corresponding interferometric coherence values change dynamically over time. Among these factors, the roles of the vegetation and its temporal variation have not yet been revealed so far. In this paper, synthetic aperture radar (Sentinel-1) data and optical remote sensing (Landsat TM) images over four whole seasons are employed to reveal the relationship between the interferometric coherence and the normalized difference vegetation index (NDVI) at five sites that have ground deformation due to mining in Henan province, China. The result showed: (1) As for the village area with few vegetation cover, the related coherence values are significantly higher than that in the farm land area with high densities of vegetation in the spring and summer, which indicates that the subsidence by mining in few vegetation cover area is easier to be monitored; (2) Linear regression coefficients ( R 2 ) between the interfereometric coherence values and the NDVI values is 0.62, which indicate the interferometric coherence values and the NDVI values change reversely in both farm land and village areas over the year. It suggests months between November and March with lower NDVI value are more suitable for deformation detecting. Therefore, the interfereometric coherence values can be used to detect the density of vegetation, while NDVI values can be reference for elucidating when the traditional differential interferometric synthetic aperture radar (DInSAR) could be effectively used. [ABSTRACT FROM AUTHOR]