Impact of GLONASS pseudorange inter-channel biases on satellite clock corrections

GLONASS carrier phase and pseudorange observations suffer from inter-channel biases (ICBs) because of frequency division multiple access (FDMA). Therefore, we analyze the effect of GLONASS pseudorange inter-channel biases on the GLONASS clock corrections. Different Analysis Centers (AC) eliminate the impact of GLONASS pseudorange ICBs in different ways. This leads to significant differences in the satellite and AC-specific offsets in the GLONASS clock corrections. Satellite and AC-specific offset differences are strongly correlated with frequency. Furthermore, the GLONASS pseudorange ICBs also leads to day-boundary jumps in the GLONASS clock corrections for the same analysis center between adjacent days. This in turn will influence the accuracy of the combined GPS/GLONASS precise point positioning (PPP) at the day-boundary. To solve these problems, a GNSS clock correction combination method based on the Kalman filter is proposed. During the combination, the AC-specific offsets and the satellite and AC-specific offsets can be estimated. The test results show the feasibility and effectiveness of the proposed clock combination method. The combined clock corrections can effectively weaken the influence of clock day-boundary jumps on combined GPS/GLONASS kinematic PPP. Furthermore, these combined clock corrections can improve the accuracy of the combined GPS/GLONASS static PPP single-day solutions when compared to the accuracy of each analysis center alone.     

Corn Area Extraction by the Integration of MODIS-EVI Time Series Data and China’s Environment Satellite (HJ-1) Data

In this paper, we intent to use the remotely sensed MODerate resolution Imaging Spectroradiometer (MODIS) data and China’s Environment Satellite (HJ-1) data for extracting the corn cultivated area over a regional scale. The high resolution HJ-1 data was to extract corn distribution at a small scale class with Support Vector Machine (SVM). The mean Enhanced Vegetation Index (EVI) time series curve of corn from MODIS was derived for the reference area and validated in a larger area. The MODIS-EVI time series curve derived from the reference area instead of the MODIS-EVI time series curve derived from the study area after validation, which was taken as the standard MODIS-EVI time series curve in for generating a standard MODIS-EVI image of corn. The mean absolute distance (MAD) between the standard MODIS-EVI image of corn and the MODIS-EVI time series image was used to detect the maximum possible extent of corn distribution in the study area. The results showed that the overall accuracy of the method was 82.17 %, with commission and omission errors of 16.85 and 15.40 %, respectively; at the county level, the satellite-estimated corn area and statistical data were well correlated (R ²?=?0.85, N?=?50) for the whole Jilin Province. It indicated that the MODIS data integrated with higher spatial resolution of HJ-1 satellite data could be utilized to enhance the extraction accuracy of corn cultivated area at a larger scale.     

基于ArcGIS Server的医院网络地理信息服务系统

为解决目前医院地理信息服务系统在信息共享、空间分析等方面的不足,采用ArcGIS Server组件、C#编程语言、SQL Server数据库研发了医院网络地理信息服务系统。该系统以天地图作为基础地理数据,融合医院空间与医疗信息,并部署到互联网环境中。除具有医疗专题信息的网络浏览与查询外,系统还实现了诸多空间分析、地学统计等功能,既可为市民就医提供更全面的信息查询,又可为医疗事故的突发与应急提供技术支撑。     

System structure and calibration models of intelligent photogrammetron

This paper describes the structure, geometric model and geometric calibration of Photogrammetron I—the first type of photogrammetron which is designed to be a coherent stereo photogrammetric system in which two cameras are mounted on a physical base but driven by an intelligent agent architecture. The system calibration is divided into two parts: the in-lab calibration determines the fixed parameters in advance of system operation, and the in-situ calibration keeps tracking the free parameters in real-time during the system operation. In a video surveillance set-up, prepared control points are tracked in stereo image sequences, so that the free parameters of the system can be continuously updated through iterative bundle adjustment and Kalman filtering.     

Development of a GIS data model with spatial,temporal and attribute components based on object-oriented approach

This paper presents a conceptual data model, the STA-model, for handling spatial, temporal and attribute aspects of objects in GIS. The model is developed on the basis of object-oriented modeling approach. This model includes two major parts: (a) modeling the signal objects by STA-object elements, and (b) modeling relationships between STA-objects. As an example, the STA-model is applied for modeling land cover change data with spatial, temporal and attribute components.     

A fast clonal selection algorithm for feature selection in hyperspectral imagery

Clonal selection feature selection algorithm (CSFS) based on clonal selection algorithm (CSA), a new computational intelligence approach, has been proposed to perform the task of dimensionality reduction in high-dimensional images, and has better performance than traditional feature selection algorithms with more computational costs. In this paper, a fast clonal selection feature selection algorithm (FCSFS) for hyperspectral imagery is proposed to improve the convergence rate by using Cauchy mutation instead of non-uniform mutation as the primary immune operator. Two experiments are performed to evaluate the performance of the proposed algorithm in comparison with CSFS using hyperspectral remote sensing imagery acquired by the pushbroom hyperspectral imager (PHI) and the airborne visible/infrared imaging spectrometer (AVIRIS), respectively. Experimental results demonstrate that the FCSFS converges faster than CSFS, hence providing an effective new option for dimensionality reduction of hyperspectral remote sensing imagery.     

Near real-time high-resolution airborne camera,AEROCam, for precision agriculture

Precision agriculture often relies on high-resolution imagery to delineate the variability within a field. Airborne Environmental Research Observational Camera (AEROCam) was designed to meet the needs of agriculture producers, ranchers, and researchers, who require high-resolution imagery in a near real-time environment for rapid decision support. AEROCam was developed and operated through a unique collaboration between several departments at the University of North Dakota, including the Upper Midwest Aerospace Consortium (UMAC), the School of Engineering and Mines, and flight operations at the John D. Odegard School of Aerospace Sciences. AEROCam consists of a Redlake MS4100 area-scan multi-spectral digital camera that features a 1920 × 1080 CCD array (7.4-μm detector) with 8-bit quantization. When operated at ～2 km above ground level, multispectral images with four bands in the visible and near infrared have a ground sample distance of 1 m with a horizontal extent of just over 1.6 km. Depending on the applications, flying at different altitudes can adjust the spatial resolution from 0.25 to 2 m. Rigorous spectral and radiometric calibrations allow AEROCam to be used in a variety of applications, qualitative and quantitative. Equipped with an inertial measurement unit (IMU) system, the images acquired can be geo-referenced automatically and delivered to end users near real time through our Digital Northern Great Plains system (DNGP). The images are also available to zone mapping application for precision farming (ZoneMAP), an online decision support tool for creating management zones from remote sensing imagery and data from other sources. Operational since 2004, AEROCam has flown over 250 sorties and delivered over 150,000 images to the users in the Northern Great Plains region, resulting in numerous applications in precision agriculture and resource management.     

Models for analyzing the driving force of cultivated land supply change

This paper focuses on a series of quantitative analysis models, such as grey relational analysis model, hierarchical cluster analysis model, principal component analysis model, linear regression model and elastic coefficient model. These models are used to analyze the comprehensive function and effect of driving forces systemically, including analysis on features, analysis for differentiating the primary and the secondary, analysis on comprehensive effects, analysis of elasticity, analysis of prediction. The primary and characteristic factors can be extracted by analysis of features and analysis for differentiating the primary and the secondary. Analysis on prediction and elasticity can predict the area of cultivated land in the future and find out which factors exert great influence on the cultivated land supply.     

Towards an integral strategy for modelling uncertain geospatial data

This paper endeavours to put the discussion on errors and uncertainties in geographical information systems (GISs) in a more systematic way by examining the strength and weakness of discrete objects and continous fields, the two distinct schools of spatial data modelling. In doing so, it argues that neither discrete objects nor continous fields alone provide objective and complete representations of highly complex geographical phenomena, though there are good reasons for asserting that continuous fields are better suited to modelling spatial dependence, heterogeneity and fuzzines significant in geographical reality than discrete objects. Thus, there seems to be merit in adopting an integrated model incorporating analytical capabilities of fields and generalization functions of objects, for which extended TIN (triangulated irregular network) models along with their duals (Voronoi diagrams) provide a pragmatical solution.