Top View
1
2017, 42(9): 1185-1194.
DOI: 10.13203/j.whugis20150779
Abstract：
Anunderwater topography survey is a basics part of marine science research, and the main task of hydrographic surveying and charting. The traditional underwater acoustics method that uses a survey boat as a carrier can have difficulties in coastal zones or shallow water. In recent years, the emergence and application of airborne laser bathymetry technology has made up for the technical defects of traditional underwater acoustics, providing a new method to solve the practical engineering problems. This paper introduces the basic principles and error sources in airborne laser bathymetry, and summarizes the current progress and developments The advantages and problems of airborne laser bathymetry technology in the offshore shallow water areas are identified and analyzed for reference. This paper concludes with a discussion of developing trends in this technology.
Anunderwater topography survey is a basics part of marine science research, and the main task of hydrographic surveying and charting. The traditional underwater acoustics method that uses a survey boat as a carrier can have difficulties in coastal zones or shallow water. In recent years, the emergence and application of airborne laser bathymetry technology has made up for the technical defects of traditional underwater acoustics, providing a new method to solve the practical engineering problems. This paper introduces the basic principles and error sources in airborne laser bathymetry, and summarizes the current progress and developments The advantages and problems of airborne laser bathymetry technology in the offshore shallow water areas are identified and analyzed for reference. This paper concludes with a discussion of developing trends in this technology.
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3
2014, 39(6): 641-644.
DOI: 10.13203/j.whugis20140150
Abstract：
Objective Big data is changing the world,and also posing challenges for GIS.The volume,velocity,and variety of these data challenge the data management ability of GIS,while the veracity and value is-sues of big data challenge spatial analysis theory and methods.Thus,as a tool focusing on spatial datamanagement,analysis and visualization,GIS has to make necessary adjustments and changes to meetthe big data requirements.This paper discusses the challenges based on the 5Vproperties of big data,and then,analyzes three characteristics of future GIS in the big data era,which are:①scalable data management,②data-driven modeling and data mining,and③ geo-computational visual analytic.
Objective Big data is changing the world,and also posing challenges for GIS.The volume,velocity,and variety of these data challenge the data management ability of GIS,while the veracity and value is-sues of big data challenge spatial analysis theory and methods.Thus,as a tool focusing on spatial datamanagement,analysis and visualization,GIS has to make necessary adjustments and changes to meetthe big data requirements.This paper discusses the challenges based on the 5Vproperties of big data,and then,analyzes three characteristics of future GIS in the big data era,which are:①scalable data management,②data-driven modeling and data mining,and③ geo-computational visual analytic.
4
2014, 39(5): 505-513.
DOI: 10.13203/j.whugis20140045
Abstract：
Objective First,we expound the background of rise of UAVRSS in this paper.Then,we discuss thefoundation,problem,research progress and trends for development of UAVRSS from unmannedaerial platform,flight control and navigation,data transmission and storage,data processing,sensortechnology,airspace usage policy,and so on.Third,we set forth the necessity and significance of de-velopment of UAVRSS through UAVRSS applications and practices in many related industry domain.At last,we make recommendations on development of UAVRSS from science and technology policy,industry policy,financial policy and so on,respectively.
Objective First,we expound the background of rise of UAVRSS in this paper.Then,we discuss thefoundation,problem,research progress and trends for development of UAVRSS from unmannedaerial platform,flight control and navigation,data transmission and storage,data processing,sensortechnology,airspace usage policy,and so on.Third,we set forth the necessity and significance of de-velopment of UAVRSS through UAVRSS applications and practices in many related industry domain.At last,we make recommendations on development of UAVRSS from science and technology policy,industry policy,financial policy and so on,respectively.
5
2014, 39(6): 631-640.
DOI: 10.13203/j.whugis20140135
Abstract：
Objective In this paper,we introduce the concept of the smart city,summarize its developmentprocess,analyze the construction motivation and objective of the smart city in China and elaborate thesupporting technologies for the smart city.Then we propose a smart city infrastructure which is basedon digital city,the Internet of Things(IOT)and cloud computing technologies.Smart city will a-chieve comprehensive awareness and management of people and things to provide various intelligentservices.Smart city with mass sensors will continuously collect vast amounts of data.Big data insmart city also bring many problems and challenges.To deal with those big data-related issues,wepropose a strategy mainly base on the cloud computing and data mining.After that,we presente aframework for cloud platform and propose the suggestion of establish smart city operation center.Inthe end,we look forward to a bright future for the smart city.
Objective In this paper,we introduce the concept of the smart city,summarize its developmentprocess,analyze the construction motivation and objective of the smart city in China and elaborate thesupporting technologies for the smart city.Then we propose a smart city infrastructure which is basedon digital city,the Internet of Things(IOT)and cloud computing technologies.Smart city will a-chieve comprehensive awareness and management of people and things to provide various intelligentservices.Smart city with mass sensors will continuously collect vast amounts of data.Big data insmart city also bring many problems and challenges.To deal with those big data-related issues,wepropose a strategy mainly base on the cloud computing and data mining.After that,we presente aframework for cloud platform and propose the suggestion of establish smart city operation center.Inthe end,we look forward to a bright future for the smart city.
6
Abstract：
Internet GIS based on JavaBean can be deployed into two tiers or N tiers application,which contains GIS browser,Web server,GIS application server and spatial database server.GIS browser is the Web browser which supports Java virtual machine,such as Netscape,Ineternet Explorer.Web Server is the server which supports WWW,servlet and JSP,such as iPlanet Web server.GIS application servers is the servers which finish the critical business logic,such as vector data service,image data service and DEM data service.Database server is the server which stores the spatial data.Spatial data organization on Internet is different from the traditional GIS.The goal is to access data simply and quickly.In general,it's geometry includes point,line,polygon and annotation.GeoSurfV4.0 is an example of Internet GIS software based on JavaBean.It contains four JavaBeans:GeoSurf2D,GeoSurf3D,GeoSurfTheme,GeoRouteServer.There are four different approaches to construct GIS application using component Internet GIS based on JavaBean:Java Application,Java Applet,Java Server Pages and Active Server Pages.
Internet GIS based on JavaBean can be deployed into two tiers or N tiers application,which contains GIS browser,Web server,GIS application server and spatial database server.GIS browser is the Web browser which supports Java virtual machine,such as Netscape,Ineternet Explorer.Web Server is the server which supports WWW,servlet and JSP,such as iPlanet Web server.GIS application servers is the servers which finish the critical business logic,such as vector data service,image data service and DEM data service.Database server is the server which stores the spatial data.Spatial data organization on Internet is different from the traditional GIS.The goal is to access data simply and quickly.In general,it's geometry includes point,line,polygon and annotation.GeoSurfV4.0 is an example of Internet GIS software based on JavaBean.It contains four JavaBeans:GeoSurf2D,GeoSurf3D,GeoSurfTheme,GeoRouteServer.There are four different approaches to construct GIS application using component Internet GIS based on JavaBean:Java Application,Java Applet,Java Server Pages and Active Server Pages.
7
2017, 42(9): 1330-1336.
DOI: 10.13203/j.whugis20150763
Abstract：
A new evacuation model is proposed for simulating pedestrian dynamics during emergencies. Based on the traditional cellular automata model enhanced with a finer discretization of space, each pedestrian occupies more than one cell and can move more than one cell during each time step. The operational rules of desire repulsive, and friction forces in a social force model are introduced in the proposed model. We establish an exit choosing method and the movement rules, and observe pedestrian movements to new destinations. Velocity is considered when describing dynamic processes of pedestrian behaviors during an evacuation. The characteristics of pedestrian evacuations are analysed in simulations and. The results show that the proposed model is valid for simulating pedestrian evacuation.
A new evacuation model is proposed for simulating pedestrian dynamics during emergencies. Based on the traditional cellular automata model enhanced with a finer discretization of space, each pedestrian occupies more than one cell and can move more than one cell during each time step. The operational rules of desire repulsive, and friction forces in a social force model are introduced in the proposed model. We establish an exit choosing method and the movement rules, and observe pedestrian movements to new destinations. Velocity is considered when describing dynamic processes of pedestrian behaviors during an evacuation. The characteristics of pedestrian evacuations are analysed in simulations and. The results show that the proposed model is valid for simulating pedestrian evacuation.
8
2014, 39(6): 689-694.
DOI: 10.13203/j.whugis20140153
Abstract：
Objective In recent years,the structure and functions of Virtual Geographic Environments(VGEs)have become clearer and how they could be used to support geographic analysis and geographic experi-ment has received serious attention.Based on the analysis of the characteristics of geographic experi-ments,this paper discusses the potential contributions of VGEs to traditional geographic experimenta-tion,thus to encourage researchers to perform geographic experiments based on VGEs in a fused real-ity-virtuality and collaborative way.
Objective In recent years,the structure and functions of Virtual Geographic Environments(VGEs)have become clearer and how they could be used to support geographic analysis and geographic experi-ment has received serious attention.Based on the analysis of the characteristics of geographic experi-ments,this paper discusses the potential contributions of VGEs to traditional geographic experimenta-tion,thus to encourage researchers to perform geographic experiments based on VGEs in a fused real-ity-virtuality and collaborative way.
9
2013, 38(3): 349-352.
Abstract：
Considering the importance of PSHA model in earthquakes forecasting, by using the catalog in the 200 years in China, we tried to improve the original PSHA model with combining seismic energy distribution model, and computed the probability of earthquakes in a specific area in China with time\|span T based on both the improved PSHA model and the original model. Finally the experimental results show that the improved model is more efficiency and reliable than the original one.
Considering the importance of PSHA model in earthquakes forecasting, by using the catalog in the 200 years in China, we tried to improve the original PSHA model with combining seismic energy distribution model, and computed the probability of earthquakes in a specific area in China with time\|span T based on both the improved PSHA model and the original model. Finally the experimental results show that the improved model is more efficiency and reliable than the original one.
10
2018, 43(11): 1696-1703.
DOI: 10.13203/j.whugis20170066
Abstract：
As the basis of indoor location services, indoor localization technology has received more and more attention in recent years. Aiming at the problems of high cost, limited precision and insufficient efficiency in existing indoor positioning technologies, pedestrian dead reckoning (PDR), human acti-vity recognition (HAR) and landmarks are combined to obtain more accurate pedestrian indoor localization. PDR is used to estimate the user's location, and the cumulative error of PDR is reduced by landmarks, which are sensed by HAR. In addition, to solve the initial position determination problem, a hidden Markov model that considers the characteristics of the indoor environment is applied to match the continuous trajectory. The experimental results show that the proposed method has a good performance in activity recognition and positioning accuracy, and can track the user's trajectory efficiently.
As the basis of indoor location services, indoor localization technology has received more and more attention in recent years. Aiming at the problems of high cost, limited precision and insufficient efficiency in existing indoor positioning technologies, pedestrian dead reckoning (PDR), human acti-vity recognition (HAR) and landmarks are combined to obtain more accurate pedestrian indoor localization. PDR is used to estimate the user's location, and the cumulative error of PDR is reduced by landmarks, which are sensed by HAR. In addition, to solve the initial position determination problem, a hidden Markov model that considers the characteristics of the indoor environment is applied to match the continuous trajectory. The experimental results show that the proposed method has a good performance in activity recognition and positioning accuracy, and can track the user's trajectory efficiently.
11
2020, 45(9): 1356-1366.
DOI: 10.13203/j.whugis20190346
Abstract：
Spatial heterogeneity or non-stationarity in data relationships is one of the hot topics in spatial statistics or relative application fields, while the development of local techniques forms an essential part for the relative studies. Geographically weighted regression (GWR) provides spatially varying coefficient estimates via location-specific weighted regression model calibrations, to explore spatial heterogeneities or non-stationarities, quantitatively. It has been widely used in a number of fields, and become one of the most important tools for exploring spatial heterogeneities in data relationships. We summarized the GWR basics in model calibration, result interpretation, model diagnostics, reviewed its research progress and problems in its applications, respectively. Meanwhile, we sorted out the important extensions of the basic GWR technique, particularly in applying flexible distance metric choices in GWR model calibration, multiscale parameter estimates and spatiotemporal data modeling. In addition, we also introduced the main GWR tools or software accordingly to provide the users or readers comprehensive reference and knowledge on the GWR technique.
Spatial heterogeneity or non-stationarity in data relationships is one of the hot topics in spatial statistics or relative application fields, while the development of local techniques forms an essential part for the relative studies. Geographically weighted regression (GWR) provides spatially varying coefficient estimates via location-specific weighted regression model calibrations, to explore spatial heterogeneities or non-stationarities, quantitatively. It has been widely used in a number of fields, and become one of the most important tools for exploring spatial heterogeneities in data relationships. We summarized the GWR basics in model calibration, result interpretation, model diagnostics, reviewed its research progress and problems in its applications, respectively. Meanwhile, we sorted out the important extensions of the basic GWR technique, particularly in applying flexible distance metric choices in GWR model calibration, multiscale parameter estimates and spatiotemporal data modeling. In addition, we also introduced the main GWR tools or software accordingly to provide the users or readers comprehensive reference and knowledge on the GWR technique.
12
2018, 43(12): 1861-1871.
DOI: 10.13203/j.whugis20180172
Abstract：
In recent years, the rapid development of the earth observation capability and the intelligent computing technology has provided opportunities for the advancement and even revolution of remote sensing information technology. Remote sensing data processing technology has experienced the Digi-tal Signal Processing Era from 60s to 80s of last century, which utilizes the Statistical Model as the core, and the Quantitative Remote Sensing Era from 90s marked by the Physical Model. Recently, it is developing towards Remotely Sensed Big Data Era which relies on Data Model by data-driven intelligent analysis. This paper summarizes the history of remote sensing information technology and presents the concept of remotely sensed big data and the characteristics of intelligent information extraction era. Firstly, from the view of remotely sensed big data, this paper discusses the construction of object-based remote sensing knowledge dataset and analyzes the data-driven intelligent information extraction strategy combined the knowledge of remote sensing and deep learning algorithm. Then the current status and development of intelligent algorithms represented by deep learning are introduced by typical applications on object detection, fine classification and parameter inversion based on remote sensing data. Consequently, the application potential of deep learning on intelligent information extraction in Remotely Sensed Big Data Era is discussed.
In recent years, the rapid development of the earth observation capability and the intelligent computing technology has provided opportunities for the advancement and even revolution of remote sensing information technology. Remote sensing data processing technology has experienced the Digi-tal Signal Processing Era from 60s to 80s of last century, which utilizes the Statistical Model as the core, and the Quantitative Remote Sensing Era from 90s marked by the Physical Model. Recently, it is developing towards Remotely Sensed Big Data Era which relies on Data Model by data-driven intelligent analysis. This paper summarizes the history of remote sensing information technology and presents the concept of remotely sensed big data and the characteristics of intelligent information extraction era. Firstly, from the view of remotely sensed big data, this paper discusses the construction of object-based remote sensing knowledge dataset and analyzes the data-driven intelligent information extraction strategy combined the knowledge of remote sensing and deep learning algorithm. Then the current status and development of intelligent algorithms represented by deep learning are introduced by typical applications on object detection, fine classification and parameter inversion based on remote sensing data. Consequently, the application potential of deep learning on intelligent information extraction in Remotely Sensed Big Data Era is discussed.
13
2017, 42(11): 1523-1529.
DOI: 10.13203/j.whugis20170177
Abstract：
An Unmanned Aerial Vehicle (UAV) is reusable, consisting of power system and unmanned autopilot controller; while a Unmanned Aircraft System (UAS) is a system controlled manually, automatically or independently to perform different kinds of tasks This article summarizes and analyzes the characteristics of UAV and UAS in different historical stages; secondly, this article emphasizes UAV and UAS requirements for Artificial Intelligence technologies; finally, this article discusses potential influences on UAS. A UAS usually consists of UAV platforms, payloads for tasks, datalink devices, information processing devices, and integrated support equipment. Research on UAS and UAV started from the beginning of the 20th century, and with the development of electronics, mechanics, material science, and computer science; UAS has rapidly developed over the last century, especially the latest thirty or forty years. With the rapid development of Artificial Intelligence over the first 20 years of the 21st century ushered in a new stage of UAS and UAV development.
An Unmanned Aerial Vehicle (UAV) is reusable, consisting of power system and unmanned autopilot controller; while a Unmanned Aircraft System (UAS) is a system controlled manually, automatically or independently to perform different kinds of tasks This article summarizes and analyzes the characteristics of UAV and UAS in different historical stages; secondly, this article emphasizes UAV and UAS requirements for Artificial Intelligence technologies; finally, this article discusses potential influences on UAS. A UAS usually consists of UAV platforms, payloads for tasks, datalink devices, information processing devices, and integrated support equipment. Research on UAS and UAV started from the beginning of the 20th century, and with the development of electronics, mechanics, material science, and computer science; UAS has rapidly developed over the last century, especially the latest thirty or forty years. With the rapid development of Artificial Intelligence over the first 20 years of the 21st century ushered in a new stage of UAS and UAV development.
14
2022, 47(8): 1176-1190.
DOI: 10.13203/j.whugis20210652
Abstract：
Objectives In the remote sensing (RS) big data era, intelligent interpretation of remote sensing images (RSI) is the key technology to mine the value of big RS data and promote several important applications. Traditional knowledge-driven RS interpretation methods, represented by expert systems, are highly interpretable, but generally show poor performance due to the interpretation knowledge being difficult to be completely and accurately expressed. With the development of deep learning in computer vision and other fields, it has gradually become the mainstream technology of RSI interpretation. However, the deep learning technique still has some fatal flaws in the RS field, such as poor interpretability and weak generalization ability. In order to overcome these problems, how to effectively combine knowledge inference and data learning has become an important research trend in the field of RS big data intelligent processing. Generally, knowledge inference relies on a strong domain knowledge base, but the research on RS knowledge graph (RS-KG) is very scarce and there is no available large-scale KG database for RSI interpretation now. Methods To overcome the above considerations, this paper focuses on the construction and evolution of the RS-KG for RSI interpretation and establishes the RS-KG takes into account the RS imaging mechanism and geographic knowledge. Supported by KG in the RS field, this paper takes three typical RSI interpretation tasks, namely, zero-shot RSI scene classification, interpretable RSI semantic segmentation, and large-scale RSI scene graph generation, as examples, to discuss the performance of the novel generation RSI interpretation paradigm which couples KG and deep learning. Results and Conclusions A large number of experimental results show that the combination of RS-KG inference and deep data learning can effectively improve the performance of RSI interpretation.The introduction of RS-KG can effectively improve the interpretation accuracy, generalization ability, anti-interference ability, and interpretability of deep learning models. These advantages make RS-KG promising in the novel generation RSI interpretation paradigm.
15
2018, 43(12): 1885-1898.
DOI: 10.13203/j.whugis20180251
Abstract：
Change detection for remote sensing imagery is the process to determine difference of the same object or phenomenon at different times. Real-time automatic change detection technology is of great significance for excavating potential of image data and maintaining the current situation of geospatial data. With the development of remote-sensing earth observation technology, varieties of remote-sensing sensors for different tasks have emerged. Change detection is also diversified with the coming up of multi-resolution remote-sensing data, with advanced theories and techniques developed for continuously different sensors. This paper reviews the development of multi-temporal remote sen-sing image change detection technologies and summarizes the classification system of multi-temporal remote sensing image change detection. And the latest developments in change detection research are summarized from three aspects:pre-processing, change detection strategies and accuracy assessment. This paper also points out the challenges that change detection is facing and possible countermeasures, in the hope of deepening the research into change detection technology for remote sensing images.
Change detection for remote sensing imagery is the process to determine difference of the same object or phenomenon at different times. Real-time automatic change detection technology is of great significance for excavating potential of image data and maintaining the current situation of geospatial data. With the development of remote-sensing earth observation technology, varieties of remote-sensing sensors for different tasks have emerged. Change detection is also diversified with the coming up of multi-resolution remote-sensing data, with advanced theories and techniques developed for continuously different sensors. This paper reviews the development of multi-temporal remote sen-sing image change detection technologies and summarizes the classification system of multi-temporal remote sensing image change detection. And the latest developments in change detection research are summarized from three aspects:pre-processing, change detection strategies and accuracy assessment. This paper also points out the challenges that change detection is facing and possible countermeasures, in the hope of deepening the research into change detection technology for remote sensing images.
16
2006, 31(5): 407-410.
Abstract：
Typical vegetation in eastern Asia and MODIS data were used to characterize newly-developed Enhanced Vegetation Index(EVI) and widely-used NDVI.The study found that both NDVI and EVI were good indicators for vegetation in wide range from arid to semi-humid area,yet they differed in characterizing dense vegetation: NDVI temporal profile had no clear seasonality with a flat-high curve;and EVI has a clear seasonality with a bell-shaped curve,which has closer relationship to temperature than to NDVI.This may suggest a new opportunity for vegetation study in densely vegetated region.
Typical vegetation in eastern Asia and MODIS data were used to characterize newly-developed Enhanced Vegetation Index(EVI) and widely-used NDVI.The study found that both NDVI and EVI were good indicators for vegetation in wide range from arid to semi-humid area,yet they differed in characterizing dense vegetation: NDVI temporal profile had no clear seasonality with a flat-high curve;and EVI has a clear seasonality with a bell-shaped curve,which has closer relationship to temperature than to NDVI.This may suggest a new opportunity for vegetation study in densely vegetated region.
17
2019, 44(7): 957-966.
DOI: 10.13203/j.whugis20190088
Abstract：
In China, traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space-air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar (InSAR) data from satellites are employed to obtain a global panorama of a region, highlighting these problematic locations; yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence, it makes the compilation of long displacement time-histories feasible, contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then, detailed assessments can be done through the deve-lopment of unmanned aerial vehicles (UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging (LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system (GNSS) and crack gauges, validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention.
In China, traditional methodology on early detection of natural terrain to landslides is challenging as zones most prone to slope failure are usually inaccessible due to high location and dense vegetation. This can lead to underestimation of potential landslide events to the degree of wrongly identifying unstable areas as stable. This paper provides a solution for these cases by proposing an integrated space-air-ground investigation system that allows for the early detection, real-time prediction, and warning of catastrophic geohazards. Firstly, high-resolution optical images and interferometric synthetic aperture radar (InSAR) data from satellites are employed to obtain a global panorama of a region, highlighting these problematic locations; yet results are detailed enough to provide reliable estimates of deformations at particular points along time spans of days and weeks. As consequence, it makes the compilation of long displacement time-histories feasible, contributing to the understanding of long-term landslide-driving phenomena in regions where it has been underestimated. This is called the general investigation. Then, detailed assessments can be done through the deve-lopment of unmanned aerial vehicles (UAV) for elaborating high-resolution relief maps and photogrammetric representations based on both visual images and light laser detection and ranging (LiDAR) data. The system finally allows for precise tagging of locations that warrant real-time site monitoring of displacements using global navigation satellite system (GNSS) and crack gauges, validating expecting behavior of these critical, but previously hidden hazardous locations. The overall approach makes it possible to establish a four-level comprehensive early warning system, which meets the urgent needs of the country and promotes a practical and operational application of such system in the field of geohazard prevention.
18
2021, 46(3): 335-340.
DOI: 10.13203/j.whugis20180500
Abstract：
Objectives Face swapping technology has important application value in entertainment, virtual reality, film and so on. However, existing methods are limited by face pose consistency and can not overcome the influence of occlusion. Methods We proposes a method of face swapping using convolutional neural network and tiny facet primitive. Firstly, detect the face using the cascade convolutional neural network and segment the face to determine the replacement region using fully convolutional network.Then, the Wallis transform is applied to adjust the skin color of the source image to make it consistent with the skin color of the face in the target image.After that, using facial key points detection method based on an ensemble of regression trees and Delaunay triangulation to construct the face triangulation network, then replacing faces based on tiny facet primitive. Finally, applying Poisson fusion to eliminate splicing traces between different images. Results We evaluate the performance of the proposed method compared with existing method through qualitative and quantitative experiments. Experimental results show that face segment can well solve the problem of occlusion such as cap, glasses, and hair.Moreover, when source image and target image have different face poses, replacing face area using tiny facet primitive separately performs better than using the whole face area. Conclusions Our method can well solve the problem of face pose consistency limitation and occlusion, which has certain practical application value.
19
2017, 42(2): 143-149.
DOI: 10.13203/j.whugis20160526
Abstract：
As we entered the 21 century, after more than ten years of rapid development of high resolution remote sensing satellite, the earth observation satellite system has developed from the original single satellite observation model to the present light and small satellite constellation observation model. All-weather and all-directional earth observation can be realized. The satellite constellation, communication satellites, navigation satellites and aircrafts are linked through dynamic linking network to form space-based information network to realize intelligent earth observation in the future. To make the system more intelligent and improve perception and cognition of system as well as quick response ability, earth observation brain (EOB) is proposed in this paper. EOB is the intelligent earth observation system based on events perception. In this paper, the concept model of EOB and the key technologies needed to be solved are introduced in detail, and an example is given to illustrate the process of perceptual cognition in the primary stage of the EOB. In the future, EOB can observe when, where, what change of what object to push these right information to right people in the right time and right place. Globally all kinds of users will obtain related geospatial data, information and knowledge in real time through EOB.
As we entered the 21 century, after more than ten years of rapid development of high resolution remote sensing satellite, the earth observation satellite system has developed from the original single satellite observation model to the present light and small satellite constellation observation model. All-weather and all-directional earth observation can be realized. The satellite constellation, communication satellites, navigation satellites and aircrafts are linked through dynamic linking network to form space-based information network to realize intelligent earth observation in the future. To make the system more intelligent and improve perception and cognition of system as well as quick response ability, earth observation brain (EOB) is proposed in this paper. EOB is the intelligent earth observation system based on events perception. In this paper, the concept model of EOB and the key technologies needed to be solved are introduced in detail, and an example is given to illustrate the process of perceptual cognition in the primary stage of the EOB. In the future, EOB can observe when, where, what change of what object to push these right information to right people in the right time and right place. Globally all kinds of users will obtain related geospatial data, information and knowledge in real time through EOB.
20
2011, 36(2): 199-203.
Abstract：
Matching line of MVLL algorithm was investigated for line-array CCD images,and it can be concluded that the matching line was the line between top feature and its corresponding bottom feature and can be regarded as a straight line.For MVLL algorithms,the difficulties of discontinuity feature that usually matched to the bottom were analyzed,and the algorithm of self-adaptive adjustment of correlation parameters was improved.After the process of self-adaptive window extension algorithm was given.The matching experiments were done on discontinuity features using two ADS40 line-array image datasets.The results show that,after applying the self-adaptive window extension algorithm,discontinuity features could be successfully matched and the self-adaptive function of MVLL algorithm was thus enhanced.
Matching line of MVLL algorithm was investigated for line-array CCD images,and it can be concluded that the matching line was the line between top feature and its corresponding bottom feature and can be regarded as a straight line.For MVLL algorithms,the difficulties of discontinuity feature that usually matched to the bottom were analyzed,and the algorithm of self-adaptive adjustment of correlation parameters was improved.After the process of self-adaptive window extension algorithm was given.The matching experiments were done on discontinuity features using two ADS40 line-array image datasets.The results show that,after applying the self-adaptive window extension algorithm,discontinuity features could be successfully matched and the self-adaptive function of MVLL algorithm was thus enhanced.
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