Satellite Interferometric Synthetic Aperture Radar(InSAR)is widely used for topographic,geological and natural resource investigations.However,most of the existing InSAR studies of ground deformation are based on rela...Satellite Interferometric Synthetic Aperture Radar(InSAR)is widely used for topographic,geological and natural resource investigations.However,most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors.This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets,to address cases when partial overlaps and/or temporal gaps exist.A new Power Exponential Knothe Model(PEKM)fits and fuses overlaps in the deformation curves,while a Long Short-Term Memory(LSTM)neural network predicts and fuses any temporal gaps in the series.Taking the city of Wuhan(China)as experiment area,COSMO-SkyMed(2011-2015),TerraSAR-X(2015-2019)and Sentinel-1(2019-2021)SAR datasets were fused to map long-term surface deformation over the last decade.An independent 2011-2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison.The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset.The correlation coefficient of the overall results is 0.78,which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation curve.The experimental results are consistent with existing studies of surface deformation at Wuhan,demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.展开更多
Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The ...Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.展开更多
Optical and Synthetic Aperture Radar(SAR)remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications,yet further advances are viable through the e...Optical and Synthetic Aperture Radar(SAR)remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications,yet further advances are viable through the exploitation of novel sensor data and imaging modes,big data and high-performance computing,advanced and automated analysis methods.This paper showcases the main research avenues in this field,with a focus on archaeological prospection and heritage site protection.Six demonstration use-cases with a wealth of heritage asset types(e.g.excavated and still buried archaeological features,standing monuments,natural reserves,burial mounds,paleo-channels)and respective scientific research objectives are presented:the Ostia-Portus area and the wider Province of Rome(Italy),the city of Wuhan and the Jiuzhaigou National Park(China),and the Siberian“Valley of the Kings”(Russia).Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite(e.g.Copernicus Sentinels and ESA Third Party Missions)and aerial(e.g.Unmanned Aerial Vehicles,UAV)platforms,as well as field-based evidence and ground truth,auxiliary topographic data,Digital Elevation Models(DEM),and monitoring data from geodetic campaigns and networks.The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes,identify threats to cultural heritage assets due to ground instability and urban development in large metropolises,and monitor post-disaster impacts in natural reserves.展开更多
Since the twenty-first century,with the rapid development of high-resolution earth observation satellites,the earth observation satellite system has developed from the initial single satellite observation model to the...Since the twenty-first century,with the rapid development of high-resolution earth observation satellites,the earth observation satellite system has developed from the initial single satellite observation model to the current satellite constellation formed by light and small satellites observation model.All-weather and all-directional fine earth observation can now be realized.In the future,the satellite constellation,communication satellites,navigation satellites,and aircrafts are linked through dynamic linking network to form an air-space information network to realize real-time services of intelligent air-space information.To further enhance the perception,cognition,and quick response ability of the network,we propose the concept and model of the Earth Observation Brain(EOB)−the intelligent earth system based on events perception in this paper.Then,some key technologies needed to be solved in the EOB are also described.An application example is illustrated to show the process of perception and cognition in the primary stage of the EOB.In the future,EOB can observe what change of what object,the when and where to push these right information to mobile terminal of right people at the right time and right place.Global users can obtain any data,information,and knowledge in real-time through the EOB.展开更多
We explored the potential of the environment and disaster monitoring and forecasting small satellite constellations (HJ-1A/1B satellites) charge-coupled device (CCD) imagery (spatial resolution of 30 m, revisit time o...We explored the potential of the environment and disaster monitoring and forecasting small satellite constellations (HJ-1A/1B satellites) charge-coupled device (CCD) imagery (spatial resolution of 30 m, revisit time of 2 days) in the monitoring of total suspended sediment (TSS) concentrations in dynamic water bodies using Poyang Lake, the largest freshwater lake in China, as an example. Field surveys conducted during October 17-26, 2009 showed a wide range of TSS concentration (3-524 mg/L). Atmospheric correction was implemented using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module in ENVI with the aid of aerosol information retrieved from concurrent Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) surveys, which worked well at the CCD bands with relatively high reflectance. A practical exponential retrieval algorithm was created between satellite remote sensing reflectance and in-situ measured TSS concentration. The retrieved results for the whole water area matched the in-situ data well at most stations. The retrieval errors may be related to the problem of scale matching and mixed pixel. In three selected subregions of Poyang Lake, the distribution trend of retrieved TSS was consistent with that of the field investigation. It was shown that HJ-1A/1B CCD imagery can be used to estimate TSS concentrations in Poyang Lake over synoptic scales after applying an appropriate atmospheric correction method and retrieval algorithm.展开更多
The indoor positioning system is now an important technique as part of the Internet-of-Things(IoT)ecosystem.Among indoor positioning techniques,multiple Wi-Fi Access Points(APs)-based positioning systems have been res...The indoor positioning system is now an important technique as part of the Internet-of-Things(IoT)ecosystem.Among indoor positioning techniques,multiple Wi-Fi Access Points(APs)-based positioning systems have been researched a lot.There is a lack of research focusing on the scene where only one Wi-Fi AP is available.This work proposes a hybrid indoor positioning system that takes advantage of the Fine-Timing Measurements(FTM)technique that is part of the IEEE 802.11mc standard,introduced back in 2016.The system uses one single Wi-Fi FTM AP and takes advantage of the built-in inertial sensors of the smartphone to estimate the device’s position.We explore both Loosely Coupled(LC)and Tightly Coupled(TC)integration schemes for the sensors’data fusion.Experimental results show that the proposed methods can achieve an average positioning accuracy of about 1 m without knowing the initial position.Compared with the LC integration method,the median error accuracy of the proposed TC fusion algorithm has improved by more than 52%and 67%,respectively,in the two experiments we set up.展开更多
The recent ten years witnessed the great achievements on rich applications of Geospatial Big Data across a variety of disciplines.For example,a huge number of Landsat images are utilized in mapping high-resolution glo...The recent ten years witnessed the great achievements on rich applications of Geospatial Big Data across a variety of disciplines.For example,a huge number of Landsat images are utilized in mapping high-resolution global forest cover and the global forest changes in the twenty-first century are explored(Hansen et al.2013),which is impossible without the support of geospatial big data and the related automatic processing techniques.Based on the huge enterprise registration data in China,the economic and social development situations and trends are revealed by the non-statistic data and novel approaches(Li et al.2018).City-wide fine-grained urban population distribution at building level is achieved by integrating and fusing multisource geospatial big data(Yao et al.2017),which is usually not desired in traditional research.Geospatial Big Data provides a new transforming paradigm of scientific research especially at the crossroads of broad disciplines,including but not limited to the humanities,the physical sciences,engineering,and so on.展开更多
Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acqu...Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images,which poses great challenges for subsequent applications.Therefore,an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time.In this work,we proposed a multi-scale hierarchical representation,i.e.binary partition tree,for analyzing large-scale UAV images.More precisely,we first obtained an initial partition of images by an oversegmentation algorithm,i.e.the simple linear iterative clustering.Next,we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships.Moreover,objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure.Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.展开更多
TerraSAR-X(TSX)can acquire high-resolution SAR images and due to its high orbit precision as well as its ability to acquire data from different off-nadir viewing angles,the high-precision stereo geolocation can be obt...TerraSAR-X(TSX)can acquire high-resolution SAR images and due to its high orbit precision as well as its ability to acquire data from different off-nadir viewing angles,the high-precision stereo geolocation can be obtained.In this study,we investigate the absolute geolocation accuracy of TSX high-resolution images in Wuhan,China.We present a direct stereo SAR geolocation method and analyze the 2D and 3D geoposition accuracy of two corner reflectors.The sub-meter localization accuracy was achieved using only atmospheric correction information available in the TSX metadata.展开更多
The paper gives an overview of the current status of education in geoinformatics in China.First,the paper provides a brief introduction to the history of geoinformatics education in China and a general review of the s...The paper gives an overview of the current status of education in geoinformatics in China.First,the paper provides a brief introduction to the history of geoinformatics education in China and a general review of the scientific and technological development.It then presents how the development affects the education and training in China.In the paper,universities and institutes in China that can award academic degrees related to geoinformatics are summarized,and undergraduate majors are briefly introduced.Next,the paper reports the work having been done by the national expert group on Surveying and Mapping,including the revision of discipline catalog and guide for graduate education and requirements.A list of typical curricula in geoinformatics education is suggested.Activities on promoting the graduate student exchange platform are presented.Finally,a case study of geoinformatics education in Wuhan University is discussed.展开更多
Single SAR image direct positioning is to determine the ground coordinate for each pixel in the SAR image assisted with a reference DEM.During this procedure,an iterative procedure is essentially needed to solve the u...Single SAR image direct positioning is to determine the ground coordinate for each pixel in the SAR image assisted with a reference DEM.During this procedure,an iterative procedure is essentially needed to solve the uncertainty in elevation of each pixel in the SAR image.However,such an iterative procedure may suffer from the problem of divergence in shaded and serious layover areas.To investigate this problem,we performed a theoretical analysis on the convergence conditions that has not been intensively studied till now.The Range-Doppler(RD)model was simplified and then the general surface is degenerated into a planar surface.Mathematical deduction was then carried out to derive the convergence conditions and the impact factors for the convergence speed were evaluated.The theoretical findings were validated by experiments for both simulated and real scenarios.展开更多
Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt t...Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt to changes in the access distribution of hotspots.By taking advantage of the spatiotemporal locality,the sequential features in tile access patterns,and the cache reading performance in the burst mode,this article proposes a tile sequence replacement method,which involves structuring a Least Recently Used(LRU)stack into three portions for the different functions in cache replacement and deriving an expression for the temporal locality and popularity of the relevant tile to facilitate the replacement process.Based on the spatial characteristics of both the tiles and the cache burst mode with regard to reading data,the proposed method generates multiple tile sequences to reflect spatiotemporal locality in tile access patterns.Then,we measure the caching value by a technique based on a weighted-based method.This technique draws on the recent access popularity and low caching costs of tile sequences,with the aim of balancing the temporal and spatial localities in tile access.It ranks tile sequences in a replacement queue to adapt to the changes in accessed hotspots while reducing the replacement frequency.Experimental results show that the proposed method effectively improves the hit rate and utilization rate for a limited distributed cache while achieving satisfactory response performance and high throughput for users in an NGIS.Therefore,it can be adapted to handle numerous data access requests in NGISs in a cloud-based environment.展开更多
Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture...Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture a huge amount of visual data with low-cost UAVs.As a kind of rapid,flexible and low-cost data acquisition system,UAVs have shown great potential to perform numerous surveying,mapping.展开更多
This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation(ID:32278),sub-project Multi-baseline SAR Processing for 3 D/4 D Rec...This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation(ID:32278),sub-project Multi-baseline SAR Processing for 3 D/4 D Reconstruction(ID:322782).The work here reported focuses on two important aspects of SAR remote sensing of tropical forests,namely the retrieval of forest biomass and the assessment of effects due to changing weather conditions.Recent studies have shown that by using SAR tomography the backscattered power at 30 m layer above the ground is linearly correlated to the forest Above Ground Biomass(AGB).However,the two parameters that determine this linear relationship might vary for different tropical forest sites.For purpose of solving this problem,we investigate the possibility of using Li DAR derived AGB to help training the two parameters.Experimental results obtained by processing data from the Tropi SAR campaign support the feasibility of the proposed concept.This analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging,for which we simulate BIOMASS repeat pass tomography using ground-based Tropi SCAT data with a revisit time of 3 days and rainy days included.The resulting backscattered power variation at 30 m is within 1.5 d B.For this forest site,this error is translated into an AGB error of about 50~80 t/hm^(2),which is 20%or less of forest AGB.展开更多
This article attempts to describe the role of tessellated models of space within the discipline of geographic information systems(GIS)—a speciality coming largely out of geography and land surveying,where there was a...This article attempts to describe the role of tessellated models of space within the discipline of geographic information systems(GIS)—a speciality coming largely out of geography and land surveying,where there was a strong need to represent information about the land’s surface within a computer system rather than on the original paper maps.We look at some of the basic operations in GIS,including dynamic and kinetic applications.We examine issues of topology and data structures and produce a tessellation model that may be widely applied both to traditional“object”and“field”data types.Based on this framework,it can be argued that tessellation models are fundamental to our understanding and processing of geographical space,and provide a coherent framework for understanding the“space”in which we exist.This first article examines static structures,and a subsequent article looks at“change”—what happens when things move.展开更多
Multi-temporal InSAR technique can implement continuous earth surface deformation detection with long time scale and wide geographical coverage.In this paper,we first employ the Small Baseline Subset method to survey ...Multi-temporal InSAR technique can implement continuous earth surface deformation detection with long time scale and wide geographical coverage.In this paper,we first employ the Small Baseline Subset method to survey potential landslides in Guide County,Qinghai Province,which is identified as a loess landslide prone area for geological and climate conditions.Two anomalous deformation regions are detected by L-band Phased Array and L-band Synthetic Aperture Radar stacks.Then,qualitative and quantitative evaluations of the measuring points are given for understanding the distribution regularity of deformation.Finally,preliminary correlation between the time-series deformation and triggering factors is analyzed to explore the driving mechanism for landslide movement.The results demonstrate that L-band SAR has high potential in landslide monitoring applications and can be used as the basis for landslide recognizing,precursory information extracting,and early warning.展开更多
In this paper seven of the ten Water Control Zones(WCZs)in Hong Kong's coastal waters with monthly or bi-weekly sampling data of 17 parameters collected at 37 monitoring stations from 1988 to 1999 were selected to...In this paper seven of the ten Water Control Zones(WCZs)in Hong Kong's coastal waters with monthly or bi-weekly sampling data of 17 parameters collected at 37 monitoring stations from 1988 to 1999 were selected to analyze the spatial and temporal variations of chlorophyll-a and its influencing factors.Cluster analysis was employed to group the monitoring stations based on the structure of the data set.Multiple step regression was employed to determine the significant influencing factors of chlorophyll-a level.The results suggest that all the monitoring stations could be grouped into two clusters.ClusterⅠwith frequent red tide incidents comprises two WCZs which are semi-enclosed bays.ClusterⅡwith less red tide occurrence comprises the other five WCZs in an estuarine environment in the west.For both clusters,the organic contents indicator,BODS,was a common significant influencing factor of the chlorophyll-a level.Nitrogen and light penetration condition related to turbidity,total volatile solids and suspended solids had more influence on the cholophyll-a level in ClusterⅠthan in ClusterⅡ,while phosphorus and oceanographic conditions associated with salinity,temperature,dissolved oxygen and pH were more important in ClusterⅡthan in ClusterⅠ.Generally,there was a higher average chlorophyll-a level in winter and autumn in a year.The chlorophyll-a level was much higher in ClusterⅠthan in ClusterⅡamong all seasons.Although the chlorophyll-a concentration had great variations from place to place in Hong Kong's coastal waters,it seemed to have a common long term fluctuation period of 8-10 years with a high-low-high variation in the period in the whole region,which might be influenced by other factors of global scale.展开更多
Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics i...Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics is related to the entire methodology rather than the individual specific analysis. In this paper, six techniques concerning big data analytics are proposed, which include: (1) Ensemble analysis related to a large volume of data, (2) Association analysis related to unknown data sampling, (3) High-dimensional analysis related to a variety of data, (4) Deep analysis related to the veracity of data, (5) Precision analysis related to the veracity of data, and (6) Divide-and-conquer analysis related to the velocity of data.The essential of big data analytics is the structural analysis of big data in an optimal criterion of physics, computation, and human cognition. fundamentally, two theoretical challenges, ie the violation of independent and identical distribution, and the extension of general set-theory, are posed. In particular, we have illustrated three kinds of association in geographical big data, ie geometrical associations in space and time, spatiotemporal correlations in statistics, and space-time relations in semantics. furthermore, we have illustrated three kinds of spatiotemporal data analysis, ie measurement (observation) adjustment of geometrical quantities, human spatial behavior analysis with trajectories, data assimilation of physical models and various observations, from which spatiotemporal big data analysis may be largely derived.展开更多
Big data is a highlighted challenge for many fields with the rapid expansion of large-volume, complex, and fast-growing sources of data. Mining from big data is required for exploring the essence of data and providing...Big data is a highlighted challenge for many fields with the rapid expansion of large-volume, complex, and fast-growing sources of data. Mining from big data is required for exploring the essence of data and providing meaningful information. To this end, we have previously introduced the theory of physical field to explore relations between objects in data space and proposed a framework of data field to discover the underlying distribution of big data. This paper concerns an overview of big data mining by the use of data field. It mainly discusses the theory of data field and different aspects of applications including feature selection for high-dimensional data, clustering, and the recognition of facial expression in human-computer interaction. In these applications, data field is employed to capture the intrinsic distribution of data objects for selecting meaningful features, fast clustering, and describing variation of facial expression. It is expected that our contributions would help overcome the problems in accordance with big data.展开更多
The impact of Brown Carbon (BrC) to aerosol light absorption has been paid more attention recently and there are a large number of studies showing that the influence of BrC on radiative forcing should not be ignored.B...The impact of Brown Carbon (BrC) to aerosol light absorption has been paid more attention recently and there are a large number of studies showing that the influence of BrC on radiative forcing should not be ignored.BrC also acts as an important component of haze pollution which is occurring frequently in Wuhan,China.Therefore,it is essential to estimate their optical properties,composition,and mass concentration.Considering most haze pollution happens during the coldest time,we retrieved BrC columnar content during winter in Wuhan for the first time.Our method bases on the fact that BrC showed the strong spectral dependence on UV-light absorption.Using this method,we found that BrC makes up the small proportions of total aerosol volume (less than 10％).In the winter of 2011,we retrieved the daily-averaged columnar-integrated mass concentration of BrC on clear day is 4.353 mg/m2 while that of haze day is 12.750 mg/m2.According to the sensitivity study,we found that the results highly relied on the assumed aerosol refractive index.To reduce the uncertainty of this approach,we need to gain a better understanding of the temporal variability of the radiation absorbing components of these aerosols in the future.展开更多
基金funded by the National Natural Science Foundation of China[grant number 42250610212]the China Scholarship Council[No.202106270150].
文摘Satellite Interferometric Synthetic Aperture Radar(InSAR)is widely used for topographic,geological and natural resource investigations.However,most of the existing InSAR studies of ground deformation are based on relatively short periods and single sensors.This paper introduces a new multi-sensor InSAR time series data fusion method for time-overlapping and time-interval datasets,to address cases when partial overlaps and/or temporal gaps exist.A new Power Exponential Knothe Model(PEKM)fits and fuses overlaps in the deformation curves,while a Long Short-Term Memory(LSTM)neural network predicts and fuses any temporal gaps in the series.Taking the city of Wuhan(China)as experiment area,COSMO-SkyMed(2011-2015),TerraSAR-X(2015-2019)and Sentinel-1(2019-2021)SAR datasets were fused to map long-term surface deformation over the last decade.An independent 2011-2020 InSAR time series analysis based on 230 COSMO-SkyMed scenes was also used as reference for comparison.The correlation coefficient between the results of the fusion algorithm and the reference data is 0.87 in the time overlapping region and 0.97 in the time-interval dataset.The correlation coefficient of the overall results is 0.78,which fully demonstrates that the algorithm proposed in our paper achieves a similar trend as the reference deformation curve.The experimental results are consistent with existing studies of surface deformation at Wuhan,demonstrating the accuracy of the proposed new fusion method to provide robust time series for the analysis of long-term land subsidence mechanisms.
文摘Long-term navigation ability based on consumer-level wearable inertial sensors plays an essential role towards various emerging fields, for instance, smart healthcare, emergency rescue, soldier positioning et al. The performance of existing long-term navigation algorithm is limited by the cumulative error of inertial sensors, disturbed local magnetic field, and complex motion modes of the pedestrian. This paper develops a robust data and physical model dual-driven based trajectory estimation(DPDD-TE) framework, which can be applied for long-term navigation tasks. A Bi-directional Long Short-Term Memory(Bi-LSTM) based quasi-static magnetic field(QSMF) detection algorithm is developed for extracting useful magnetic observation for heading calibration, and another Bi-LSTM is adopted for walking speed estimation by considering hybrid human motion information under a specific time period. In addition, a data and physical model dual-driven based multi-source fusion model is proposed to integrate basic INS mechanization and multi-level constraint and observations for maintaining accuracy under long-term navigation tasks, and enhanced by the magnetic and trajectory features assisted loop detection algorithm. Real-world experiments indicate that the proposed DPDD-TE outperforms than existing algorithms, and final estimated heading and positioning accuracy indexes reaches 5° and less than 2 m under the time period of 30 min, respectively.
基金supported by the European Space Agency(ESA)and the National Remote Sensing Center(NRSCC)-Ministry of Science and Technology(MOST)of the P.R.China under[grant number 58113]ESA[contract number 4000135360/21/I-NB,grant numbers 190791 and PP0085498]+3 种基金the German Aerospace Center(DLR)[grant number MTH3764]the Italian Space Agency(ASI)[COSMO-SkyMed license WUHAN-CSK]Planet Labs PBC under the Education and Research Program[grant number 412519]the National Natural Science Foundation of China[grant number 42250610212].
文摘Optical and Synthetic Aperture Radar(SAR)remote sensing has a long history of use and reached a good level of maturity in archaeological and cultural heritage applications,yet further advances are viable through the exploitation of novel sensor data and imaging modes,big data and high-performance computing,advanced and automated analysis methods.This paper showcases the main research avenues in this field,with a focus on archaeological prospection and heritage site protection.Six demonstration use-cases with a wealth of heritage asset types(e.g.excavated and still buried archaeological features,standing monuments,natural reserves,burial mounds,paleo-channels)and respective scientific research objectives are presented:the Ostia-Portus area and the wider Province of Rome(Italy),the city of Wuhan and the Jiuzhaigou National Park(China),and the Siberian“Valley of the Kings”(Russia).Input data encompass both archive and newly tasked medium to very high-resolution imagery acquired over the last decade from satellite(e.g.Copernicus Sentinels and ESA Third Party Missions)and aerial(e.g.Unmanned Aerial Vehicles,UAV)platforms,as well as field-based evidence and ground truth,auxiliary topographic data,Digital Elevation Models(DEM),and monitoring data from geodetic campaigns and networks.The novel results achieved for the use-cases contribute to the discussion on the advantages and limitations of optical and SAR-based archaeological and heritage applications aimed to detect buried and sub-surface archaeological assets across rural and semi-vegetated landscapes,identify threats to cultural heritage assets due to ground instability and urban development in large metropolises,and monitor post-disaster impacts in natural reserves.
基金substantially supported by the National Natural Science Foundation of China[grant number 91438203]the National Basic Research Program of China(973 Program)[grant number 2014CB744201].
文摘Since the twenty-first century,with the rapid development of high-resolution earth observation satellites,the earth observation satellite system has developed from the initial single satellite observation model to the current satellite constellation formed by light and small satellites observation model.All-weather and all-directional fine earth observation can now be realized.In the future,the satellite constellation,communication satellites,navigation satellites,and aircrafts are linked through dynamic linking network to form an air-space information network to realize real-time services of intelligent air-space information.To further enhance the perception,cognition,and quick response ability of the network,we propose the concept and model of the Earth Observation Brain(EOB)−the intelligent earth system based on events perception in this paper.Then,some key technologies needed to be solved in the EOB are also described.An application example is illustrated to show the process of perception and cognition in the primary stage of the EOB.In the future,EOB can observe what change of what object,the when and where to push these right information to mobile terminal of right people at the right time and right place.Global users can obtain any data,information,and knowledge in real-time through the EOB.
基金Supported by the National Basic Research Program of China(973Program)(No.2011CB707106)the National Natural Science Foundation of China(Nos.41071261,41023001,41021061,40906092,40971193,41101415)+3 种基金the Opening Foundation of Institute of Remote Sensing and Earth Sciences,Hangzhou Normal University(No.PDKF2010YG06)the Fundamental Research Funds for the Central Universities,the China Postdoctoral Science Foundation(No.20100480861)LIESMARS Special Research Funding,the Natural Science Foundation of Hubei Province,China(No.2009CDB107)the Natural Science Foundation of Zhejiang Province,China(No.Y5090143)
文摘We explored the potential of the environment and disaster monitoring and forecasting small satellite constellations (HJ-1A/1B satellites) charge-coupled device (CCD) imagery (spatial resolution of 30 m, revisit time of 2 days) in the monitoring of total suspended sediment (TSS) concentrations in dynamic water bodies using Poyang Lake, the largest freshwater lake in China, as an example. Field surveys conducted during October 17-26, 2009 showed a wide range of TSS concentration (3-524 mg/L). Atmospheric correction was implemented using the Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) module in ENVI with the aid of aerosol information retrieved from concurrent Terra/Moderate Resolution Imaging Spectroradiometer (MODIS) surveys, which worked well at the CCD bands with relatively high reflectance. A practical exponential retrieval algorithm was created between satellite remote sensing reflectance and in-situ measured TSS concentration. The retrieved results for the whole water area matched the in-situ data well at most stations. The retrieval errors may be related to the problem of scale matching and mixed pixel. In three selected subregions of Poyang Lake, the distribution trend of retrieved TSS was consistent with that of the field investigation. It was shown that HJ-1A/1B CCD imagery can be used to estimate TSS concentrations in Poyang Lake over synoptic scales after applying an appropriate atmospheric correction method and retrieval algorithm.
基金supported by the National Key Research and Development Program of China[grant numbers 2016YFB0502200,2016YFB0502201]the NSFC[grant number 91638203]。
文摘The indoor positioning system is now an important technique as part of the Internet-of-Things(IoT)ecosystem.Among indoor positioning techniques,multiple Wi-Fi Access Points(APs)-based positioning systems have been researched a lot.There is a lack of research focusing on the scene where only one Wi-Fi AP is available.This work proposes a hybrid indoor positioning system that takes advantage of the Fine-Timing Measurements(FTM)technique that is part of the IEEE 802.11mc standard,introduced back in 2016.The system uses one single Wi-Fi FTM AP and takes advantage of the built-in inertial sensors of the smartphone to estimate the device’s position.We explore both Loosely Coupled(LC)and Tightly Coupled(TC)integration schemes for the sensors’data fusion.Experimental results show that the proposed methods can achieve an average positioning accuracy of about 1 m without knowing the initial position.Compared with the LC integration method,the median error accuracy of the proposed TC fusion algorithm has improved by more than 52%and 67%,respectively,in the two experiments we set up.
文摘The recent ten years witnessed the great achievements on rich applications of Geospatial Big Data across a variety of disciplines.For example,a huge number of Landsat images are utilized in mapping high-resolution global forest cover and the global forest changes in the twenty-first century are explored(Hansen et al.2013),which is impossible without the support of geospatial big data and the related automatic processing techniques.Based on the huge enterprise registration data in China,the economic and social development situations and trends are revealed by the non-statistic data and novel approaches(Li et al.2018).City-wide fine-grained urban population distribution at building level is achieved by integrating and fusing multisource geospatial big data(Yao et al.2017),which is usually not desired in traditional research.Geospatial Big Data provides a new transforming paradigm of scientific research especially at the crossroads of broad disciplines,including but not limited to the humanities,the physical sciences,engineering,and so on.
基金This work was supported in part by the National Key Basic Research and Development Program of China[grant number 2013CB733404]the National Natural Science Foundation of China[grant number 61271401],[grant number 91338113].
文摘Unmanned aerial vehicle(UAV)-based imaging systems have many superiorities compared with other platforms,such as high flexibility and low cost in collecting images,providing wide application prospects.However,the acquisition of the UAV-based image commonly results in very high resolution and very large-scale images,which poses great challenges for subsequent applications.Therefore,an efficient representation of large-scale UAV images is necessary for the extraction of the required information in a reasonable time.In this work,we proposed a multi-scale hierarchical representation,i.e.binary partition tree,for analyzing large-scale UAV images.More precisely,we first obtained an initial partition of images by an oversegmentation algorithm,i.e.the simple linear iterative clustering.Next,we merged the similar superpixels to build an object-based hierarchical structure by fully considering the spectral and spatial information of the superpixels and their topological relationships.Moreover,objects of interest and optimal segmentation were obtained using object-based analysis methods with the hierarchical structure.Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake and the mosaic of images in the South-west of Munich demonstrate the effectiveness and efficiency of our proposed method.
基金This work was supported by the National Natural Science Foundation of China[grant number 61331016]and[grant number 41174120].The TerraSAR-X data were provided by DLR via the LAN2245 Project.
文摘TerraSAR-X(TSX)can acquire high-resolution SAR images and due to its high orbit precision as well as its ability to acquire data from different off-nadir viewing angles,the high-precision stereo geolocation can be obtained.In this study,we investigate the absolute geolocation accuracy of TSX high-resolution images in Wuhan,China.We present a direct stereo SAR geolocation method and analyze the 2D and 3D geoposition accuracy of two corner reflectors.The sub-meter localization accuracy was achieved using only atmospheric correction information available in the TSX metadata.
基金The work is supported by the National Basic Research Program of China(973 Program)(grant number 2011CB707105)the National Natural Science Foundation of China(grant number 41271397)the Program for New Century Excellent Talents in University(grant number NCET-13-0435).
文摘The paper gives an overview of the current status of education in geoinformatics in China.First,the paper provides a brief introduction to the history of geoinformatics education in China and a general review of the scientific and technological development.It then presents how the development affects the education and training in China.In the paper,universities and institutes in China that can award academic degrees related to geoinformatics are summarized,and undergraduate majors are briefly introduced.Next,the paper reports the work having been done by the national expert group on Surveying and Mapping,including the revision of discipline catalog and guide for graduate education and requirements.A list of typical curricula in geoinformatics education is suggested.Activities on promoting the graduate student exchange platform are presented.Finally,a case study of geoinformatics education in Wuhan University is discussed.
基金The authors would like to thank the German Aerospace Center(DLR)for providing the test data-sets via the DLR AO LAN0793 and LAN0634,and Prof.Miaozhong Xu of LIESMARS for providing the photogrammetric DEM with spatial resolution of 3 mThis work was supported by the National Natural Science Foundation of China[grant number 41271457]the Demonstration System of High Resolution Remote Sensing Applications in Urban Fine Management Area[grant number 06-Y30B04–9002-13/15].
文摘Single SAR image direct positioning is to determine the ground coordinate for each pixel in the SAR image assisted with a reference DEM.During this procedure,an iterative procedure is essentially needed to solve the uncertainty in elevation of each pixel in the SAR image.However,such an iterative procedure may suffer from the problem of divergence in shaded and serious layover areas.To investigate this problem,we performed a theoretical analysis on the convergence conditions that has not been intensively studied till now.The Range-Doppler(RD)model was simplified and then the general surface is degenerated into a planar surface.Mathematical deduction was then carried out to derive the convergence conditions and the impact factors for the convergence speed were evaluated.The theoretical findings were validated by experiments for both simulated and real scenarios.
基金This work was supported by the National Natural Science Foundation of China[grant number 41371370]the National Basic Research Program of China[grant number 2012CB719906].
文摘Specific features of tile access patterns can be applied in a cache replacement strategy to a limited distributed high-speed cache for the cloud-based networked geographic information services(NGISs),aiming to adapt to changes in the access distribution of hotspots.By taking advantage of the spatiotemporal locality,the sequential features in tile access patterns,and the cache reading performance in the burst mode,this article proposes a tile sequence replacement method,which involves structuring a Least Recently Used(LRU)stack into three portions for the different functions in cache replacement and deriving an expression for the temporal locality and popularity of the relevant tile to facilitate the replacement process.Based on the spatial characteristics of both the tiles and the cache burst mode with regard to reading data,the proposed method generates multiple tile sequences to reflect spatiotemporal locality in tile access patterns.Then,we measure the caching value by a technique based on a weighted-based method.This technique draws on the recent access popularity and low caching costs of tile sequences,with the aim of balancing the temporal and spatial localities in tile access.It ranks tile sequences in a replacement queue to adapt to the changes in accessed hotspots while reducing the replacement frequency.Experimental results show that the proposed method effectively improves the hit rate and utilization rate for a limited distributed cache while achieving satisfactory response performance and high throughput for users in an NGIS.Therefore,it can be adapted to handle numerous data access requests in NGISs in a cloud-based environment.
文摘Unmanned Aerial Vehicles(UAVs)have been involved in a wide range of remote sensing applications.In particular,recent developments in robotics,computer vision,and geomatics technologies have made it possible to capture a huge amount of visual data with low-cost UAVs.As a kind of rapid,flexible and low-cost data acquisition system,UAVs have shown great potential to perform numerous surveying,mapping.
文摘This paper is intended to report on the progresses made during the Dragon-4 project Three and Four-Dimensional Topographic Measurement and Validation(ID:32278),sub-project Multi-baseline SAR Processing for 3 D/4 D Reconstruction(ID:322782).The work here reported focuses on two important aspects of SAR remote sensing of tropical forests,namely the retrieval of forest biomass and the assessment of effects due to changing weather conditions.Recent studies have shown that by using SAR tomography the backscattered power at 30 m layer above the ground is linearly correlated to the forest Above Ground Biomass(AGB).However,the two parameters that determine this linear relationship might vary for different tropical forest sites.For purpose of solving this problem,we investigate the possibility of using Li DAR derived AGB to help training the two parameters.Experimental results obtained by processing data from the Tropi SAR campaign support the feasibility of the proposed concept.This analysis is complemented by an assessment of the impact of changing weather conditions on tomographic imaging,for which we simulate BIOMASS repeat pass tomography using ground-based Tropi SCAT data with a revisit time of 3 days and rainy days included.The resulting backscattered power variation at 30 m is within 1.5 d B.For this forest site,this error is translated into an AGB error of about 50~80 t/hm^(2),which is 20%or less of forest AGB.
文摘This article attempts to describe the role of tessellated models of space within the discipline of geographic information systems(GIS)—a speciality coming largely out of geography and land surveying,where there was a strong need to represent information about the land’s surface within a computer system rather than on the original paper maps.We look at some of the basic operations in GIS,including dynamic and kinetic applications.We examine issues of topology and data structures and produce a tessellation model that may be widely applied both to traditional“object”and“field”data types.Based on this framework,it can be argued that tessellation models are fundamental to our understanding and processing of geographical space,and provide a coherent framework for understanding the“space”in which we exist.This first article examines static structures,and a subsequent article looks at“change”—what happens when things move.
基金The authors would like to thank Japan Aerospace Exploration Agency(JAXA)for providing the PALSAR data sets via the ALOS-RA4 project(PI1440)This work was supported by the National Key Basic Research Program of China[grant number 2013CB733205].
文摘Multi-temporal InSAR technique can implement continuous earth surface deformation detection with long time scale and wide geographical coverage.In this paper,we first employ the Small Baseline Subset method to survey potential landslides in Guide County,Qinghai Province,which is identified as a loess landslide prone area for geological and climate conditions.Two anomalous deformation regions are detected by L-band Phased Array and L-band Synthetic Aperture Radar stacks.Then,qualitative and quantitative evaluations of the measuring points are given for understanding the distribution regularity of deformation.Finally,preliminary correlation between the time-series deformation and triggering factors is analyzed to explore the driving mechanism for landslide movement.The results demonstrate that L-band SAR has high potential in landslide monitoring applications and can be used as the basis for landslide recognizing,precursory information extracting,and early warning.
基金National Natural Science Foundation of China,No.47176032
文摘In this paper seven of the ten Water Control Zones(WCZs)in Hong Kong's coastal waters with monthly or bi-weekly sampling data of 17 parameters collected at 37 monitoring stations from 1988 to 1999 were selected to analyze the spatial and temporal variations of chlorophyll-a and its influencing factors.Cluster analysis was employed to group the monitoring stations based on the structure of the data set.Multiple step regression was employed to determine the significant influencing factors of chlorophyll-a level.The results suggest that all the monitoring stations could be grouped into two clusters.ClusterⅠwith frequent red tide incidents comprises two WCZs which are semi-enclosed bays.ClusterⅡwith less red tide occurrence comprises the other five WCZs in an estuarine environment in the west.For both clusters,the organic contents indicator,BODS,was a common significant influencing factor of the chlorophyll-a level.Nitrogen and light penetration condition related to turbidity,total volatile solids and suspended solids had more influence on the cholophyll-a level in ClusterⅠthan in ClusterⅡ,while phosphorus and oceanographic conditions associated with salinity,temperature,dissolved oxygen and pH were more important in ClusterⅡthan in ClusterⅠ.Generally,there was a higher average chlorophyll-a level in winter and autumn in a year.The chlorophyll-a level was much higher in ClusterⅠthan in ClusterⅡamong all seasons.Although the chlorophyll-a concentration had great variations from place to place in Hong Kong's coastal waters,it seemed to have a common long term fluctuation period of 8-10 years with a high-low-high variation in the period in the whole region,which might be influenced by other factors of global scale.
基金This study is supported jointly by the Fundamental Research Funds for the Central Universities, the Key Project of National Natural Science Foundation of China [grant number 41331175, and the LIESMARS Special Research Funding
文摘Big data have 4V characteristics of volume, variety, velocity, and veracity, which authentically calls for big data analytics. However, what are the dominant characteristics of big data analysis? Here, the analytics is related to the entire methodology rather than the individual specific analysis. In this paper, six techniques concerning big data analytics are proposed, which include: (1) Ensemble analysis related to a large volume of data, (2) Association analysis related to unknown data sampling, (3) High-dimensional analysis related to a variety of data, (4) Deep analysis related to the veracity of data, (5) Precision analysis related to the veracity of data, and (6) Divide-and-conquer analysis related to the velocity of data.The essential of big data analytics is the structural analysis of big data in an optimal criterion of physics, computation, and human cognition. fundamentally, two theoretical challenges, ie the violation of independent and identical distribution, and the extension of general set-theory, are posed. In particular, we have illustrated three kinds of association in geographical big data, ie geometrical associations in space and time, spatiotemporal correlations in statistics, and space-time relations in semantics. furthermore, we have illustrated three kinds of spatiotemporal data analysis, ie measurement (observation) adjustment of geometrical quantities, human spatial behavior analysis with trajectories, data assimilation of physical models and various observations, from which spatiotemporal big data analysis may be largely derived.
文摘Big data is a highlighted challenge for many fields with the rapid expansion of large-volume, complex, and fast-growing sources of data. Mining from big data is required for exploring the essence of data and providing meaningful information. To this end, we have previously introduced the theory of physical field to explore relations between objects in data space and proposed a framework of data field to discover the underlying distribution of big data. This paper concerns an overview of big data mining by the use of data field. It mainly discusses the theory of data field and different aspects of applications including feature selection for high-dimensional data, clustering, and the recognition of facial expression in human-computer interaction. In these applications, data field is employed to capture the intrinsic distribution of data objects for selecting meaningful features, fast clustering, and describing variation of facial expression. It is expected that our contributions would help overcome the problems in accordance with big data.
基金This work was financially supported by National Natural Science Foundation of China [grant number 41627804,41401498,and 41601044],the National Key Research and Development Program of China [grant number 2017YFC0212600] and Natural Science Foundation of Hubei Province (grant number 2017CFB404)
文摘The impact of Brown Carbon (BrC) to aerosol light absorption has been paid more attention recently and there are a large number of studies showing that the influence of BrC on radiative forcing should not be ignored.BrC also acts as an important component of haze pollution which is occurring frequently in Wuhan,China.Therefore,it is essential to estimate their optical properties,composition,and mass concentration.Considering most haze pollution happens during the coldest time,we retrieved BrC columnar content during winter in Wuhan for the first time.Our method bases on the fact that BrC showed the strong spectral dependence on UV-light absorption.Using this method,we found that BrC makes up the small proportions of total aerosol volume (less than 10％).In the winter of 2011,we retrieved the daily-averaged columnar-integrated mass concentration of BrC on clear day is 4.353 mg/m2 while that of haze day is 12.750 mg/m2.According to the sensitivity study,we found that the results highly relied on the assumed aerosol refractive index.To reduce the uncertainty of this approach,we need to gain a better understanding of the temporal variability of the radiation absorbing components of these aerosols in the future.
