Interferometric synthetic aperture radar (InSAR) time-series analysis provides high spatial resolution and continuous temporal coverage for investigations of long-term ground displacement. Beijing, ...the capital city of China, has suffered from land subsidence since the 1950s, and extreme groundwater extraction has led to subsidence rates of >100mm/year. In this study, InSAR time-series analysis is performed on different data subsets to investigate the ground displacement at Capital International Airport, Beijing, between June 2003 and November 2013. The results show that the ground surface in the airport has deformed at different rates ranging from −66.2mm/year (sinking) to 8.2mm/year (uplift) relative to the reference point. The projected vertical displacement rates agreed with measurements estimated from ground-leveling surveys, and the correlation coefficient of the fitting result is 0.96, with a standard deviation of 0.9mm/year and a mean different of 2.0mm/year. The runways and terminals have been affected by land subsidence to various degrees. Previous studies has indicated that long-term intense groundwater extraction is the main reason leading to land subsidence in this area. Other triggering factors, such as active faults, the quaternary compressible layers and urbanization, also have different degrees of contribution or impact on land subsidence in Beijing Plain. Furthermore, some interesting behaviors from groundwater (such as inter- and semi-annual variations) and subsidence, the relationship between them are also found in this study.
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•3-stacked InSAR time-series analysis are well validated by ground leveling.•Measurement of the cumulative displacements of the runways•Relationship between land subsidence and anthropogenic activities, geological condition
Object counting is a fundamental task in remote sensing analysis. Nevertheless, it has been barely studied compared with object counting in natural images due to the challenging factors, e.g., ...background clutter and scale variation. This paper proposes a triple attention and scale-aware network (TASNet). Specifically, a triple view attention (TVA) module is adopted to remedy the background clutter, which executes three-dimension attention operations on the input tensor. In this case, it can capture the interaction dependencies between three dimensions to distinguish the object region. Meanwhile, a pyramid feature aggregation (PFA) module is employed to relieve the scale variation. The PFA module is built in a four-branch architecture, and each branch has a similar structure composed of dilated convolution layers to enlarge the receptive field. Furthermore, a scale transmit connection is introduced to enable the lower branch to acquire the upper branch’s scale, increasing the output’s scale diversity. Experimental results on remote sensing datasets prove that the proposed model can address the issues of background clutter and scale variation. Moreover, it outperforms the state-of-the-art (SOTA) competitors subjectively and objectively.
Janus transition-metal dichalcogenides (TMDCs) are emerging as special 2D materials with different chalcogen atoms covalently bonded on each side of the unit cell, resulting in interesting ...properties. To date, several synthetic strategies have been developed to realize Janus TMDCs, which first involves stripping the top-layer S of MoS2 with H atoms. However, there has been little discussion on the intermediate Janus MoSH. It is critical to find the appropriate plasma treatment time to avoid sample damage. A thorough understanding of the formation and properties of MoSH is highly desirable. In this work, a controlled H2-plasma treatment has been developed to gradually synthesize a Janus MoSH monolayer, which was confirmed by the TOF-SIMS analysis as well as the subsequent fabrication of MoSSe. The electronic properties of MoSH, including the high intrinsic carrier concentration (∼2 × 1013 cm–2) and the Fermi level (∼ – 4.11 eV), have been systematically investigated by the combination of FET device study, KPFM, and DFT calculations. The results demonstrate a method for the creation of Janus MoSH and present the essential electronic parameters which have great significance for device applications. Furthermore, owing to the metallicity, 2D Janus MoSH might be a potential platform to observe the SPR behavior in the mid-infrared region.
Land subsidence is a global environmental geological hazard caused by natural or human activities. The high spatial resolution and continuous time coverage of interferometric synthetic aperture radar ...(InSAR) time series analysis techniques provide data and a basis for the development of methods for the investigation and evolution mechanism study of regional land subsidence. Beijing, the capital city of China, has suffered from land subsidence for decades since it was first recorded in the 1950s. It was reported that uneven ground subsidence and fractures have seriously affected the normal operation of the Beijing Capital International Airport (BCIA) in recent years before the overhaul of the middle runway in April 2017. In this study, InSAR time series analysis was successfully used to detect the uneven local subsidence and ground fissure activity that has been gradually increasing in BCIA since 2010. A multi-temporal InSAR (MT-InSAR) technique was performed on 63 TerraSAR-X/TanDem-X (TSX/TDX) images acquired between 2010 and 2017, then deformation rate maps and time series for the airport area were generated. Comparisons of deformation rate and displacement time series from InSAR and ground-leveling were carried out in order to evaluate the accuracy of the InSAR-derived measurements. After an integrated analysis of the distribution characteristics of land subsidence, previous research results, and geological data was carried out, we found and located an active ground fissure. Then main cause of the ground fissures was preliminarily discussed. Finally, it can be conducted that InSAR technology can be used to identify and monitor geological processes, such as land subsidence and ground fissure activities, and can provide a scientific approach to better explore and study the cause and formation mechanism of regional subsidence and ground fissures.
Crowd counting in congested scenes is a crucial yet challenging task in video surveillance and urban security system. The performance of crowd counting has been greatly boosted with the rapid ...development of deep learning. However, robust crowd counting in high-density environment with scale variations remains under-explored. To address this problem, we propose a dual attention-aware network (
DA
2
Net) for robust crowd counting in dense crowd scene with scale variations. Specifically, the
DA
2
Net consists of two modules, namely Spatial Attention (SA) module and Channel Attention (CA) module. The SA module focuses on the spatial dependencies in the whole feature map to locate the heads accurately. The CA module attempts to handle the relations between channel maps and highlights the discriminative information in specific channels. Thus, it alleviates the mistaken estimation for background regions. The interactions between SA module and CA module provide the synergy which facilitates the learning of discriminative features with a focus on the essential head region. Experimental results on five benchmark datasets, i.e., ShanghaiTech, UCF_CC_50, UCF-QNRF, WorldExpo’10, and NWPU, demonstrate that the
DA
2
Net can achieve the state-of-the-art performance on both accuracy and robustness.
Person re-identification is a challenging research issue in computer vision and has a broad application prospect in intelligent security. In recent years, with the emergence of large-scale person ...datasets and the rapid development of deep learning, many outstanding results have been achieved in person re-identification researches, which mainly involves two critical technologies: feature extraction and distance metric. Among them, feature extraction has been well summarized in the current literature of person re-identification, but there is no systematic analysis of the distance metric method in the current review literature. However, effective and reliable distance metric is crucial to improve the accuracy of person re-identification. Therefore, it is necessary to systematically review and summarize the metric learning methods in person re-identification, so as to provide some references for the researchers of metric learning. In this paper, we make a comprehensive analysis of metric learning methods in the past five years, which can be summarized into three aspects: distance metric method, metric learning algorithm, and re-ranking for the metric results. Then, we compare the performance of some representative metric learning methods and discuss them in-depth. Finally, we make a prospect for the future research direction of metric learning in person re-identification.
Land subsidence is the ground surface response to underground space development, utilization and evolution. Presently, land subsidence has developed into a global, comprehensive and interdisciplinary ...complex systems problem. More than half a century has passed since the discovery of subsidence in the Beijing Plain in the 1960s. In this study, we investigate the land subsidence in the Beijing Plain over the period of 2003–2015 using ENVISAT ASAR and RADARSAT-2 interferometric datasets and the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique. Furthermore, we introduced the data field model and index-based built-up index (IBI) to obtain the dynamic and static load information of the Beijing Plain. Then, based on a machine learning method, we selected the gradient lifting decision tree (GBDT) model to quantitatively analyze the contributions of groundwater level change, compressible deposit thickness and dynamic and static loads to land subsidence. The results showed that the maximum land subsidence rate was 122 and 141 mm/year in 2003–2010 and 2010–2015, respectively. Comparisons between the SBAS-InSAR results and leveling measurements showed that the minimum absolute error achieved was only 0.2 mm/year. We suggest that the groundwater exploitation in the third confined aquifer has greater impacts on land subsidence in the Beijing Plain than the other factors. The land subsidence likely occurred in compressible deposit thicknesses exceeding 90 m. Moreover, we found that the compressible thickness and groundwater level contributions to land subsidence exceeded 60%. Our results provide a scientific basis for the regulation and control of regional land subsidence.
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•The causes of land subsidence in Beijing Plain are complex.•The maximum land subsidence rate is 122 and 141 mm/year in 2003–2010 and 2010–2015.•The dynamic load by using a spatial data mining method of data field model.•The third confined aquifer groundwater has greater impacts on land subsidence.•Compressible thickness and groundwater contribution to land subsidence exceed 60%.
Correlation filters (CF) based tracking methods have attracted considerable attentions for their competitive performance. However, the inherent issues of boundary effect and filter degradation, as ...well as the scale variation, degrade the tracking accuracy. In addition, the frame-by-frame updating strategy limits the tracking speed, especially in those deep features-based CF trackers. To address these issues, we propose a novel tracker, namely Accelerated Duality-aware Correlation Filters (ADCF), in this paper. In the proposed tracker, dual correlation filters,
i.e.
, translation filter and scale filter, are designed for target localization and scale estimation, respectively. A spatio-temporal regularization term is employed to suppress the boundary effect and filter degradation. Moreover, a model updating strategy named Sparse learning-based Average Peak-to-Correlation Energy (S-APCE) is proposed to accelerate the tracking speed. Finally, an Alternating Direction Method of Multipliers (ADMM) formulation is developed to optimize the ADCF efficiently. Extensive experimental results over six tracking benchmarks prove that the proposed tracker outperforms the state-of-the-art (SOTA) trackers in tracking accuracy and speed.
•A new time-series fusion method to blend SAR data from multi-platforms.•Adaption of the new fusion method in detecting subsidence impacting the BHSR.•Identification of the most severe subsidence ...sections along the BHSR.•Relationship between subsidence and its causes using a maximum entropy model.
Beijing-Tianjin High Speed Railway is the first high-speed railway in China. The Beijing section of it runs through areas affected by subsidence which threaten its safe operation. This study develops a new time series fusion method based on the minimum gradient difference of a fitting curve to produce time series subsidence along this section. Through blending Envisat ASAR and TerrSAR-X time series, the InSAR-derived subsidence and its spatial–temporal development was analyzed along the railway. The relationship between subsidence and its causes was then explored using a maximum entropy model. The study reveals that: (1) The subsidence dynamics identified using the new fusion method agrees with the ground deformation measurements; (2) The sections of most severe subsidence occur between kilometer point KP 11 and KP 21; and (3) The main hydrogeological factors affecting subsidence are the compressible deposit thickness and the groundwater level in the second confined aquifer. The new fusion method proposed improves the accuracy and reliability of subsidence time series. It extends the time span of subsidence monitoring. The approach is mainly applicable to areas with significant vertical deformation, and is particularly suitable for integrating multi-platform data with overlapping in time or with a short time gap.
The long-term overexploitation of groundwater leads to serious land subsidence and threatens the safety of Beijing-Tianjin-Hebei (BTH). In this paper, an interferometric point target analysis (IPTA) ...with small baseline subset InSAR (SBAS-InSAR) technique was used to derive the land subsidence in a typical BTH area from 2012 to 2018 with 126 Radarsat-2 and 184 Sentinel-1 images. The analysis reveals that the average subsidence rate reached 118 mm/year from 2012 to 2018. Eleven subsidence features were identified: Shangzhuang, Beijing Airport, Jinzhan and Heizhuanghu in Beijing, Guangyang and Shengfang in Langfang, Wangqingtuo in Tianjin, Dongguang in Cangzhou, Jingxian and Zaoqiang in Hengshui and Julu in Xingtai. Comparing the different types of land use in subsidence feature areas, the results show that when the land-use type is relatively more complex and superimposed with residential, industrial and agricultural land, the land subsidence is relatively more significant. Moreover, the land subsidence development patterns are different in the BTH areas because of the different methods adopted for their water resource development and utilization, with an imbalance in their economic development levels. Finally, we found that the subsidence changes are consistent with groundwater level changes and there is a lag period between land subsidence and groundwater level changes of approximately two months in Beijing Airport, Jinzhan, Jingxian and Zaoqiang, of three months in Shangzhuang, Heizhuanghu, Guangyang, Wangqingtuo and Dongguang and of four months in Shengfang.
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