With the rapid development of earth observation technology, remote sensing images have played more important roles, because the high resolution images can provide the original data for object recognition, disaster investigation, and so on. When a disastrous earthquake breaks out, a large number of roads could be damaged instantly. There are a lot of approaches about road extraction, such as region growing, gray threshold, and k-means clustering algorithm. We could not obtain the undamaged roads with these approaches, if the trees or their shadows along the roads are difficult to be distinguished from the damaged road. In the paper, a method is presented to extract the damaged road with high resolution aerial image of post-earthquake. Our job is to extract the damaged road and the undamaged with the aerial image. We utilized the mathematical morphology approach and the k-means clustering algorithm to extract the road. Our method was composed of four ingredients. Firstly, the mathematical morphology filter operators were employed to remove the interferences from the trees or their shadows. Secondly, the k-means algorithm was employed to derive the damaged segments. Thirdly, the mathematical morphology approach was used to extract the undamaged road; Finally, we could derive the damaged segments by overlaying the road networks of pre-earthquake. Our results showed that the earthquake, broken in Yaan, was disastrous for the road, Therefore, we could take more measures to keep it clear.
Vehicle detection is a very important task for intelligent transportation system. In this paper, a method with mathematical morphology and template matching is presented to detect the crowded vehicles of parking lot with high resolution aerial image. Our experimental results with high resolution aerial image showed that the graded image, with the spatial resolution of 1×1ft, could greatly reduce the calculation time, but with the same accuracy as the original image with the spatial resolution of 0.5×0.5ft .
Although influenza is a common disease with characteristics of seasonality, the determinants of each season's onset,
magnitude, and duration are poorly understood. This paper focuses on the role of environmental factors in spread and
epidemic of seasonal influenza and explores the environmental explanatory factors for different types of influenza in
mainland China. It also introduces satellite remote sensing as an important data obtaining approach, and highlights the
potential of using satellite images for monitoring dynamics of climate and landscapes related to the spread of seasonal
influenza. Applying Geographic Information System (GIS) technique combining with traditional statistical analysis, the
paper uses influenza virus isolation rate (VIR) as the measure of influenza activity and analyzed its association with
environmental factors. The results show that the spread and epidemic of influenza is influenced by various
environmental factors, among which the temperature and humidity are seemed to be the determinants. However, the
impacts of the environmental factors to different types of virus are varied. Low temperature and humidity conditions
arere associated with a higher activity of both influenza A and B. On the contrary, high temperature and humidity
conditions are associated with a higher activity of influenza A, but are associated with only a moderate or low, less
consistent increase in the activity of influenza B. Recognition of this association could lead to better understanding of the
mechanisms of emergence of influence epidemics and provide scientific evidence for controlling influenza.
KEYWORDS: Data modeling, Systems modeling, Process modeling, Prototyping, Logic, Databases, Data storage, Human-machine interfaces, Decision support systems, Computer programming
Models are often thought as the abstraction of object, phenomenon, system and process. But the present model base system is good at the abstraction of process which starts from the input data to the results. And it falls short of the model composition. Based on the object-oriented methods, this paper aims to discuss a new application-oriented model base system. The structure of model interface parameter is abstracted into descriptive model (DM) which can be regarded a bridge between different models. Using object-oriented method, a series researches has been made focused on DM, and
establish the application-oriented model-base system. The model working flow and user-oriented model inheritance mechanism were designed for applying and maintaining the model resource easily. A prototype system was designed and developed, and an application demonstration is shown to verify its feasibility.
KEYWORDS: Data modeling, Systems modeling, Mathematical modeling, Cognitive modeling, Decision support systems, Statistical analysis, Statistical modeling, Process modeling, Databases, Data storage
The present object-oriented model representing way have not fully addressed the issues of model inheritance for general
users, increase the difficulty of maintenance and model composition, and make the interrelation among models more
complex. This paper aims to make improvement in model presenting way and put forward a new model-base system
frame, which can implement model inheritance for general users and its data and method are thought separately of as
descriptive model (DM) and operative model (OM). The definition of operative model and descriptive ones, model
representing way, correlation and how to ensure their consistency and inter-dependency were discussed in detail. Based
on the frame, our group developed STA-MMS which can be incorporated into other decision support system (DSS) to
manage models and to help users to build new models by reusing existing model resources in the system without
modifying code. The architecture of STA-MMS system and its essential functions are defined. Procedures for model
generalization, representation and composition are developed according to object-oriented concepts and methods.
Finally, we examine how STA-MMS and its associated procedures and techniques are implemented in a prototype
StaGIS to facilitate the construction, retrieval and execution of analytical models in the statistic analyzing process.
After the outbreak of highly pathogenic Avian Influenza (HPAI) in South Korea in the end of year 2003, estimates of the impact of HPAI in affected countries vary greatly, the total direct losses are about 3 billion US dollars, and it caused 15 million birds and poultry flocks death. It is significant to understand the spatial distribution and transmission characters of HPAI for its prevention and control. According to 50 outbreak cases for HPAI in Chinese mainland during 2004, this paper introduces the approach of spatial distribution and transmission characters for HPAI and its results. Its approach is based on remote sensing and GIS techniques. Its supporting data set involves normalized difference vegetation index (NDVI) and land surface temperature (Ts) derived from a time-series of remote sensing data of 1 kilometer-resolution NOAA/AVHRR, birds' migration routes, topology geographic map, lake and wetland maps, and meteorological observation data. In order to analyze synthetically using these data, a supporting platform for analysis Avian Influenza epidemic situation (SPAS/AI) was developed. Supporting by SPAS/AI, the integrated information from multi-sources can be easily used to the analysis of the spatial distribution and transmission character of HPAI. The results show that the range of spatial distribution and transmission of HPAI in China during 2004 connected to environment factors NDVI, Ts and the distributions of lake and wetland, and especially to bird migration routes. To some extent, the results provide some suggestions for the macro-decision making for the prevention and control of HPAI in the areas of potential risk and reoccurrence.
In this paper, we present a new approach to integrate Geographic Information System and remote sensing. Its implementation environment is in Grouping Interpretation System (GrIS). GrIS was developed based on application task requirements, visual interpreting procedure and manner, and multi-technique integration. GrIS can operate in both single-computer mode and multi-computer mode with client/server structure in LAN and WAN environment. This system was designed to function within an integrated Geographic Information System, remote sensing processing and image interpretation function. Moreover, it allows the incorporation of raster format with vector format for image interpretation, automatic and semi-automatic interpretation mode respectively. The integration result of image interpretation into grouping interpretation system (GrIS) is demonstrated. The use of this integration technology and the relevant information from GIS leads to an enhanced information extraction and effective analysis in remote sensing images.
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