摘要 :
Remote sensing dynamic monitoring of land use could detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper...
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Remote sensing dynamic monitoring of land use could detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper focused on the technological procedure of land use dynamic monitoring using multi-temporal remote sensed data, including the process of multi-temporal remote sensed images, the information classification and information extraction from remote sensing imagery, and analysis of land use changes. Based on multi-temporal remote sensed imagery of three periods in 1954, 1998 and 2002, Yangzhou city was chosen as the study area, and extraction after classified method had been used to monitor land use changes during 1954 to 2002. While classifying, the object-oriented method was used to extract features from different temporal imagery. The extraction results showed that the residential land in Yangzhou city increased largely from 9.72 km~2 to 21.35km~2, and the arable land decreased a great deal from 23.99 km~2 to 9.64 km~2. Urban expansion was toward to east. Finally, the main driving forces were analyzed, and multivariable linear regression model was used to explore the primary and secondary forces.
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摘要 :
Remote sensing dynamic monitoring of land use could detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper...
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Remote sensing dynamic monitoring of land use could detect the change information of land use and update the current land use map, which is important for rational utilization and scientific management of land resources. This paper focused on the technological procedure of land use dynamic monitoring using multi-temporal remote sensed data, including the process of multi-temporal remote sensed images, the information classification and information extraction from remote sensing imagery, and analysis of land use changes. Based on multi-temporal remote sensed imagery of three periods in 1954, 1998 and 2002, Yangzhou city was chosen as the study area, and extraction after classified method had been used to monitor land use changes during 1954 to 2002. While classifying, the object-oriented method was used to extract features from different temporal imagery. The extraction results showed that the residential land in Yangzhou city increased largely from 9.72 km~2 to 21.35km~2, and the arable land decreased a great deal from 23.99 km~2 to 9.64 km~2. Urban expansion was toward to east. Finally, the main driving forces were analyzed, and multivariable linear regression model was used to explore the primary and secondary forces.
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摘要 :
Remote sensing dynamic monitoring of land use could detect the change information of land use and update the current land use map,which is important for rational utilization and scientific management of land resources.This paper f...
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Remote sensing dynamic monitoring of land use could detect the change information of land use and update the current land use map,which is important for rational utilization and scientific management of land resources.This paper focused on the technological procedure of land use dynamic monitoring using multi-temporal remote sensed data,including the process of multi-temporal remote sensed images,the information classification and information extraction from remote sensing imagery,and analysis of land use changes.Based on multi-temporal remote sensed imagery of three periods in 1954,1998 and 2002,Yangzhou city was chosen as the study area,and extraction after classified method had been used to monitor land use changes during 1954 to 2002.While classifying,the object-oriented method was used to extract features from different temporal imagery.The extraction results showed that the residential land in Yangzhou city increased largely from 9.72 km2 to 21.35km2,and the arable land decreased a great deal from 23.99 km2 to 9.64 km2.Urban expansion was toward to east.Finally,the main driving forces were analyzed,and multivariable linear regression model was used to explore the primary and secondary forces.
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The infrared (IR) target recognizing and tracking technique is widely applied to many fields such as industries, navigation, weapon controlling and guiding and so on. Its application in military field has become the research hotsp...
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The infrared (IR) target recognizing and tracking technique is widely applied to many fields such as industries, navigation, weapon controlling and guiding and so on. Its application in military field has become the research hotspot. The stability of target tracking is the most important in military applications. However, it is difficult to track the aerial target because of the complex background and noise interference, especially from long distance, which make tracking targets even harder. In this paper, a novel image tracking system is designed, which uses template matching algorithm combined with Kalman filter. Because of the noise in image, the presence of occlusion, and the deformation of tracked target, some tracking algorithms may fail. So it is the main idea in this paper to merge the advantages from the tracking algorithms, and track the target real time. The algorithm for weak small targets from the image is based on template matching algorithm. In order to overcome the problems related to the changes of unpredictable circumstance, Kalman filter tracking algorithm is used. For the disadvantage of template matching algorithm towards occasions in target tracking, such as target occlusion, drastic change of image intensity, the relevant solutions are proposed. In cases when the target is occluded or moves more than the operational limits of the tracking module, Kalman filter is used to predict the object location. Thus, automatic detection and tracking of target in real-time is achieved and the proposed method is more robust in target tracking. The results show that the algorithm can realize target tracking under complicated scenes. It also improves the tracking stability, capacity of anti-interfering and running efficiency.
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摘要 :
The infrared (IR) target recognizing and tracking technique is widely applied to many fields such as industries, navigation, weapon controlling and guiding and so on. Its application in military field has become the research hotsp...
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The infrared (IR) target recognizing and tracking technique is widely applied to many fields such as industries, navigation, weapon controlling and guiding and so on. Its application in military field has become the research hotspot. The stability of target tracking is the most important in military applications. However, it is difficult to track the aerial target because of the complex background and noise interference, especially from long distance, which make tracking targets even harder. In this paper, a novel image tracking system is designed, which uses template matching algorithm combined with Kalman filter. Because of the noise in image, the presence of occlusion, and the deformation of tracked target, some tracking algorithms may fail. So it is the main idea in this paper to merge the advantages from the tracking algorithms, and track the target real time. The algorithm for weak small targets from the image is based on template matching algorithm. In order to overcome the problems related to the changes of unpredictable circumstance, Kalman filter tracking algorithm is used. For the disadvantage of template matching algorithm towards occasions in target tracking, such as target occlusion, drastic change of image intensity, the relevant solutions are proposed. In cases when the target is occluded or moves more than the operational limits of the tracking module, Kalman filter is used to predict the object location. Thus, automatic detection and tracking of target in real-time is achieved and the proposed method is more robust in target tracking. The results show that the algorithm can realize target tracking under complicated scenes. It also improves the tracking stability, capacity of anti-interfering and running efficiency.
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A multiscale segmentation method is proposed for multispectral imagery of high resolution by combining an adapted watershed algorithm and a region merging algorithm. Before the preliminary segmentation by the adapted watershed alg...
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A multiscale segmentation method is proposed for multispectral imagery of high resolution by combining an adapted watershed algorithm and a region merging algorithm. Before the preliminary segmentation by the adapted watershed algorithm, a filtering method and a method for getting rid of local minimum areas are imposed to avoid over-segmentation. The whole process can be divided into five steps as follows. A case study is conducted with a high resolution image, QuikBird, of Beijing city acquired in 2007. From the segmentation results it can be found most of urban features could be extracted correctly and the segmentation edge is accurate and smooth. And it can be concluded that the method can have more semantic information, reduce the dasiaPepper and Salt Phenomenonpsila effectively, and improve the overall classification accuracy of QuikBird image with improved computing efficiency.
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摘要 :
A multiscale segmentation method is proposed for multispectral imagery of high resolution by combining an adapted watershed algorithm and a region merging algorithm. Before the preliminary segmentation by the adapted watershed alg...
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A multiscale segmentation method is proposed for multispectral imagery of high resolution by combining an adapted watershed algorithm and a region merging algorithm. Before the preliminary segmentation by the adapted watershed algorithm, a filtering method and a method for getting rid of local minimum areas are imposed to avoid over-segmentation. The whole process can be divided into five steps as follows. A case study is conducted with a high resolution image, QuikBird, of Beijing city acquired in 2007. From the segmentation results it can be found most of urban features could be extracted correctly and the segmentation edge is accurate and smooth. And it can be concluded that the method can have more semantic information, reduce the 'Pepper and Salt Phenomenon' effectively, and improve the overall classification accuracy of QuikBird image with improved computing efficiency.
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The differences between the data integration of a dynamic database grid (DBG) and that of a distributed database system are analyzed, and three kinds of data integration strategies are given on the background of DBG based on Peer ...
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The differences between the data integration of a dynamic database grid (DBG) and that of a distributed database system are analyzed, and three kinds of data integration strategies are given on the background of DBG based on Peer to Peer (P2P) framework, including the centralized data integration (CDI) strategy, the distributed data integration (DDI) strategy and the filter-based data integration (FDDD strategy. CDI calls all the database grid services (DGSs) at a single node, DDI disperses the DGSs to multiple nodes, while FDDI schedules the data integration nodes based on filtering the keywords returned from DGSs. The performance of these three integration strategies are compared with and analyzed by simulation experiments. FDDI is more evident for filtering the keywords with data redundancy increasing. Through the reduction of large amount of data transportation, it effectively shortens the executing time for the task and improves its efficiency.
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摘要 :
The differences between the data integration of a dynamic database grid (DBG) and that of a distributed database system are analyzed, and three kinds of data integration strategies are given on the background of DBG based on Peer ...
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The differences between the data integration of a dynamic database grid (DBG) and that of a distributed database system are analyzed, and three kinds of data integration strategies are given on the background of DBG based on Peer to Peer (P2P) framework, including the centralized data integration (CDI) strategy, the distributed data integration (DDI) strategy and the filter-based data integration (FDDD strategy. CDI calls all the database grid services (DGSs) at a single node, DDI disperses the DGSs to multiple nodes, while FDDI schedules the data integration nodes based on filtering the keywords returned from DGSs. The performance of these three integration strategies are compared with and analyzed by simulation experiments. FDDI is more evident for filtering the keywords with data redundancy increasing. Through the reduction of large amount of data transportation, it effectively shortens the executing time for the task and improves its efficiency.
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Improving the ability of disaster prevention and reduction is a major task which our society faces. When a disaster occurs, the freely flowing of the heterogeneous distributed spatial information and the efficient integrated shari...
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Improving the ability of disaster prevention and reduction is a major task which our society faces. When a disaster occurs, the freely flowing of the heterogeneous distributed spatial information and the efficient integrated sharing are the important guarantee of disaster prevention and reduction. In this paper, a new integrated idea and proposal is proposed, which is based on the Service Oriented Architecture (SOA)-based spatial information integration of earthquake disaster prevention and reduction. Through some experiments, the SOA integration and loading of various clients is verified, which demonstrates the integrated application of the spatial data-sharing function of the earthquake disaster prevention and reduction, and the validity of the proposed model and schema.
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