共查询到17条相似文献，搜索用时 78 毫秒
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分析了传统点位不确定性指标的局限性,基于信息论中的联合熵和最大熵定理导出了n维随机点熵不确定指标以及落入其内概率的统一公式;提出了以熵误差椭圆与熵误差椭球作为2维、3维GIS中点元的位置不确定性度量指标.提出的熵指标具有唯一确定、不受置信水平选取的主观性影响等特点,适合于度量未知分布的点位不确定性. 相似文献
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GIS中空间数据不确定性的混合熵模型研究 总被引：4，自引：0，他引：4
基于信息理论和模糊集合理论，针对GIS中部分空间数据既具有随机性又具有模糊性的特点，建立了空间数据不确定性的混合熵模型。以GIS中线元不确定性为例，讨论了线元不确定性的统计熵、模糊熵和混合熵估计方法，并针对特例给出了线元不确定性的熵带分布。 相似文献
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熵理论在确定点位不确定性指标上的应用 总被引：3，自引：0，他引：3
分析了传统点位不确定性指标的局限性，基于信息论中的联合熵和最大熵定理导出了n维随机点熵不确定指标以及落入其内概率的统一公式；提出了以熵误差椭圆与熵误差椭球作为2维、3维GIS中点元的位置不确定性度量指标。提出的熵指标具有唯一确定、不受置信水平选取的主观性影响等特点，适合于度量未知分布的点位不确定性。 相似文献
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GIS中三维空间直线的误差熵模型 总被引：1，自引：0，他引：1
从信息熵的角度提出了三维空间直线的误差熵模型，该模型由以垂直直线的平面误差熵为半径的圆柱体和两端点的误差球组成，是一种完全确定的度量空间线元不确定性的模型。理论分析与实验表明，本文所提出的模型具有较好的效果。 相似文献
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030 30 1 GIS中面元的误差熵模型 /李大军 (武汉大学 )… / /测绘学报 .- 2 0 0 3,32 ( 1) .- 31～ 35根据整个线元边缘分布的平均信息熵确定了“ε -带”的宽度 ,提出了线元的平均误差熵带模型 ,进一步扩展到面元的误差熵环模型 ,误差熵环的带宽取构成边界线的各线段误差熵的加权平均值 ,通过算例进行了比较 ,并绘出了其可视化图形。0 30 30 2 GIS属性数据精度的缺陷率度量的统计模型 /刘春(香港理工大学 )… / /测绘学报 .- 2 0 0 3,32 ( 1) .36～ 41基于抽样检验在测量数据精度分析中的思想 ,提出基于抽样的缺陷率方法 ,对GIS属性… 相似文献
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利用光斑的特性确定激光点位在光斑中的不确定性,将误差熵引入到激光点位不确定性的评价中。根据激光反射特性,确定了激光点位不确定性的概率密度函数,利用信息熵的定义推导了激光点位的信息熵,同时,利用信息熵与误差熵的关系进行了激光点位误差熵的推导,根据误差熵关系式确定了误差熵与光斑面积的线性关系。根据点云光斑实际面积,得到了点云误差熵及每个激光点位的平均误差熵。利用入射角与误差熵之间的关系,分析了入射角对激光点位不确定性的影响程度,确定了扫描的最佳入射角范围。通过设置不同扫描间隔得到的点云数据,验证了利用误差熵对点云不确定性进行评价的可行性。 相似文献
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Positional error of line segments is usually described by using “g-band”, however, its band width is in relation to the confidence level choice. In fact, given different confidence levels, a series of concentric bands can be obtained. To overcome the effect of confidence level on the error indicator, by introducing the union entropy theory, we propose an entropy error ellipse index of point, then extend it to line segment and polygon, and establish an entropy error band of line segment and an entropy error donut of polygon. The research shows that the entropy error index can be determined uniquely and is not influenced by confidence level, and that they are suitable for positional uncertainty of planar geometry features. 相似文献
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GONGJianya DUDaosheng LIDajun GUANYunlan 《地球空间信息科学学报》2003,6(2):20-24
Positional error of line segments is usually described byusing “g-band”,however,its band width is in relation to the confidence level choice.In fact,given different confidence levels,a series of concentric bands can be obtained.To overcome the effect of confidence level on the error indicator,by introducing the union entropy theory,we propose an entropy error ellipse index of point,then extend it to line segment and polygon.and establish an entropy error band of line segment and an entropy error do-nut of polygon.The research shows that the entropy error index can be determined uniquely and is not influenced by confidence level,and that they are suitable for positional uncertainty of planar geometry features. 相似文献
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SHI Yufeng SHI Wenzhong 《地球空间信息科学学报》2007,10(1):61-66
Spatial data uncertainty can directly affect the quality of digital products and GIS-based decision making. On the basis of the characteristics of randomicity of positional data and fuzziness of attribute data, taking entropy as a measure, the stochastic entropy model of positional data uncertainty and fuzzy entropy model of attribute data uncertainty are proposed. As both randomicity and fuzziness usually simultaneously exist in linear segments, their omnibus effects are also investigated and quantified. A novel uncertainty measure, general entropy, is presented. The general entropy can be used as a uniform measure to quantify the total uncertainty caused by stochastic uncertainty and fuzzy uncertainty in GIS. 相似文献
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Spatial data uncertainty can directly affect the quality of digital products and GIS-based decision making. On the basis of the characteristics of randomicity of positional data and fuzziness of attribute data, taking entropy as a measure, the stochastic entropy model of positional data uncertainty and fuzzy entropy model of attribute data uncertainty are proposed. As both randomicity and fuzziness usually simultaneously exist in linear segments, their omnibus effects are also investigated and quantified. A novel uncertainty measure, general entropy, is presented. The general entropy can be used as a uniform measure to quantify the total uncertainty caused by stochastic uncertainty and fuzzy uncertainty in GIS. 相似文献
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SHI Yufeng SHI Wenzhong School of Architecture Engineering Shandong University of Technology Zhangzhou Road Zibo China Key Laboratoryof Geospace Environment Geodesy Ministry of Education Wuhan University Luoyu Road Wuhan China. 《地球空间信息科学学报》2007,10(1):61-66
Spatial data uncertainty can directly affect the quality of digital products and GIS-based decision making. On the basis of the characteristics of randomicity of positional data and fuzziness of attribute data, taking entropy as a measure, the stochastic entropy model of positional data uncertainty and fuzzy entropy model of attribute data uncertainty are proposed. As both randomic-ity and fuzziness usually simultaneously exist in linear segments, their omnibus effects are also investigated and quantified. A novel uncertainty measure, general entropy, is presented. The general entropy can be used as a uniform measure to quantify the total un-certainty caused by stochastic uncertainty and fuzzy uncertainty in GIS. 相似文献
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平面随机线元等概率密度误差模型边界包络线 总被引：1，自引：0，他引：1
线状实体误差模型包络线既是GIS位置不确定性研究的重要内容,又是GIS可视化研究的关键指标.为了充分利用计算机技术求解符合GIS精度要求的误差模型包络线,基于文献[1,2]中探讨过的等概率密度误差模型建模机理和数值算法,研究了平面随机线元等概率密度误差模型边界包络线的确定原理和计算方法,并通过实例辅以可视化分析,验证了原理的正确性和可操作性. 相似文献
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Analytical Modelling of Positional and Thematic Uncertainties in the Integration of Remote Sensing and Geographical Information Systems 总被引：1，自引：0，他引：1
This paper describes three aspects of uncertainty in geographical information systems (GIS) and remote sensing. First, the positional uncertainty of an area object in a GIS is discussed as a function of positional uncertainties of line segments and boundary line features. Second, the thematic uncertainty of a classified remote sensing image is described using the probability vectors from a maximum likelihood classification. Third, the "S-band" model is used to quantify uncertainties after combining GIS and remote sensing data. 相似文献