基于核密度估计的点群密度制图应用研究

随着信息技术的发展,点状专题数据激增,需要突破传统制图方法的表达局限,利用海量点群数据对专题信息进行快速、直观、综合的制图表达。文中以南京市银行的ATM网点数据以及全国KTV营业点分布为制图数据源,探讨了点群数据核密度估计图以及三维核密度曲面图的制作方法,并对其中的关键技术作了深入的研究。研究表明,基于核密度估计的网点密度制图方法,消除了边界分隔对密度计算精度的影响,能更好反映点群数据的空间分布密度、区域差异、动态变化等特征,具备更丰富的表现力。     

基于核密度估计方法的地图色域分析

针对地图制图中色彩选择较为困难的问题,该文收集国内外优秀地图、地图集、著作等上成功的设色方案,提取大量地图色彩样本。利用核密度估计方法,分析各类地图的常用色域区间,作为地图配色时的基色调。根据大量的地图色彩样本,利用定量的数学方法以及数据挖掘方法,探索地图色域特征分布规律。最后以收集的人口地图和环保地图颜色样本为例进行实验分析,对比证明本文方法能够很好地缩小地图色域选择区间,方便制图者根据地图种类选择颜色。     

核密度估计的道路匹配方法探讨

针对目前地图合并技术的同名实体匹配中线实体(以道路为例)研究的不足,文章基于密度插值和DEM分析,提出一种针对线实体进行全局匹配的方法:采用积分的思想将核密度估计方法扩展应用于线实体密度计算,通过提取其密度谷脊线,将其应用于地图合并技术领域中的线实体匹配研究主题,指导相似度指标阈值的选取,并取得了较好的效果,为实现同一地区不同来源或不同比例尺(主要是相近比例尺)地图数据合并及数据库集成提供了参考。     

一种改进的加权整体最小二乘GNSS船姿估计方法

为提高GNSS船姿测量精度,该文构建了基于先验模糊度信息的乘性欧拉角GNSS载波观测方程,提出了一种基于部分变量误差模型(Partial-EIV)的加权整体最小二乘(WTLS)动态船姿估计方法。首先利用GNSS动对动加以基线长约束快速精确固定模糊度,然后将整周模糊度固定解作为已知值代入乘性姿态角载波观测误差方程,并将观测方程进行向量化;针对状态方程以及观测方程系数矩阵包含随机元素和固定元素的结构特征,引入Partial-EIV模型,采用一种改进目标函数的WTLS方法估计乘性欧拉角,该方法可以同时顾及状态方程以及观测方程误差,充分利用观测值以及姿态约束信息,减小误差累积。通过实测GNSS三天线匀速测姿算例对本文方法进行验证,结果表明,该文方法优于动态参考站差分(MBD)基线姿态直接解法,对于复杂海洋环境船姿测量具有一定的参考价值。     

半参数模型估计的补偿最小二乘法与正则核方法

本文根据测量数据处理中利用半参数对系统误差去除的效果,提出了半参数补偿最小二乘法和正则核方法两种方法进行比较.两种去除系统误差的方法都存在着局限性,应该根据系统误差的具体情况,选择不同的方法;最后通过模拟算例去除系统误差,并与实测数据进行拟合验证两种方法的可行性.     

核密度法的南京苏果超市分布热点探测

针对超市经营者进行选址优化和空间配置问题,该文以南京市主城区内苏果超市为例,利用平面核密度方法,对城市内零售超市的空间结构与分布模式进行分析,系统地研究不同类型超市的空间分布特征并进行热点区域的探测。研究表明,热点区域的探测结果符合商业网点分布的一般规律,南京主城区内不同类型苏果超市的空间分布表现出较大的空间区域分异:购物广场的热点分布于中心城区外围的次一级中心,社区店和标准超市的热点分布于居住区,便利店的热点大多分布于人口密集区域。     

一种改进的机载SAR基线估计方法

本文利用地面控制点对机载双天线雷达系统的基线参数进行估计.首先证实了基线长度的精度与控制点斜距差的精度是同数量级的,然后运用最小二乘法解算控制点的斜距差,并通过干涉相位信息和解缠相位信息对斜距相位差进行精化处理,得到精度较高的斜距相位差参加基线的解算.通过实验表明,该方法获得的基线参数精度较高.     

空间核密度的城市域雨量监测计选址方法

针对雨量监测计传统选址方法未顾及城市域复杂环境的问题,该文探索了城市域雨量监测计的选址条件,提出了基于空间核密度分析的雨量监测计选址模型。以空间核密度分析方法实现雨量计选址较好地平衡了城市域选址的地理环境约束;以雨量计设备信号传输等属性信息辅助并结合GIS缓冲区分析实现了雨量计选址的优选;利用3次实测降雨数据结合交叉验证方法验证了雨量计选址的合理性。实验结果表明,所制定选址条件及选址方法在城市域中具有较好适用性,对水文设备(积水计等)的选址分析具有较好的借鉴意义。     

一种高郁闭度林分树高的估测方法

针对单一无人机影像无法有效地提取高郁闭度林分树高的问题,该文提出一种结合无人机影像数据和全站仪测量的地形数据获取高郁闭度林分树高的方法。①利用搭载数码相机的小型无人机平台,以50m航高获取实验区局部高精度林分影像,利用全站仪获取实验区的地形数据;②利用无人机影像处理软件对影像进行处理,通过初步的几何校正以及空三加密过程得到整个实验区的高分辨率DEM和DOM模型;③采用局部最大值算法探测单株林木的树冠中心点坐标,利用自然生长算法和高程差值公式得到树冠中心对应的树根高程;④以树冠中心点高程以及树根高程的差值作为单木树高的估计值。通过实验得出:结合无人机影像与全站仪数据能够准确快速地获取高郁闭度林分树高,本文提出的方法可以为森林可持续经营提供数据基础。     

ISVR: an improved synthetic variable ratio method for image fusion

An Improved Synthetic Variable Ratio (ISVR) fusion method is proposed to merge high spatial resolution panchromatic (Pan) images and multispectral (MS) images based on a simulation of the panchromatic image from the multispectral bands. Compared to the existing SVR (Synthetic Variable Ratio) family methods, the ISVR method manifests two major improvements: a simplified and physically meaningful scheme to derive the parameters necessary as required by SVR, and less computing power. Two sets of IKONOS Pan and MS images: one in urban area and another one in a forest area, were used to evaluate the effectiveness of classification-oriented ISVR method in comparison to the Principal Component Substitution (PCS), Synthetic Variable Ratio (SVR) and Gram-Schmidt Spectral Sharpening (GS) methods that are available in the ENVI software package. Results indicate the ISVR method achieves the best spectral fidelity to facilitate classification compared to PCS, SVR, and GS methods.     

A new kernel method for hyperspectral image feature extraction

Abstract

Hyperspectral image provides abundant spectral information for remote discrimination of subtle differences in ground covers. However, the increasing spectral dimensions, as well as the information redundancy, make the analysis and interpretation of hyperspectral images a challenge. Feature extraction is a very important step for hyperspectral image processing. Feature extraction methods aim at reducing the dimension of data, while preserving as much information as possible. Particularly, nonlinear feature extraction methods (e.g. kernel minimum noise fraction (KMNF) transformation) have been reported to benefit many applications of hyperspectral remote sensing, due to their good preservation of high-order structures of the original data. However, conventional KMNF or its extensions have some limitations on noise fraction estimation during the feature extraction, and this leads to poor performances for post-applications. This paper proposes a novel nonlinear feature extraction method for hyperspectral images. Instead of estimating noise fraction by the nearest neighborhood information (within a sliding window), the proposed method explores the use of image segmentation. The approach benefits both noise fraction estimation and information preservation, and enables a significant improvement for classification. Experimental results on two real hyperspectral images demonstrate the efficiency of the proposed method. Compared to conventional KMNF, the improvements of the method on two hyperspectral image classification are 8 and 11%. This nonlinear feature extraction method can be also applied to other disciplines where high-dimensional data analysis is required.     

An accurate selectivity estimation method for window queries and an implementation thereof

Spatial selectivity estimation is crucial to choose the cheapest execution plan for a given query in a query optimizer. This article proposes an accurate spatial selectivity estimation method based on the cumulative density (CD) histograms, which can deal with any arbitrary spatial query window. In this method, the selectivity can be estimated in original logic of the CD histogram, after the four corner values of a query window have been accurately interpolated on the continuous surface of the elevation histogram. For the interpolation of any corner points, we first identify the cells that can affect the value of point (x, y) in the CD histogram. These cells can be categorized into two classes: ones within the range from (0, 0) to (x, y) and the other overlapping the range from (0, 0) to (x, y). The values of the former class can be used directly, whereas we revise the values of any cells falling in the latter class by the number of vertices in the corresponding cell and the area ratio covered by the range from (0, 0) to (x, y). This revision makes the estimation method more accurate. The CD histograms and estimation method have been implemented in INGRES. Experiment results show that the method can accurately estimate the selectivity of arbitrary query windows and can help the optimizer choose a cheaper query plan.     

An improved hierarchical clustering method based on the k-NN and density peak clustering

Clustering is one of the most prevalent and important data mining algorithms ever developed. Currently, most clustering methods are divided into distance-based and density-based. In 2014, the fast search and find of density peaks clustering method was proposed, which is simple and effective and has been extensively applied in several research domains. However, the original version requires manually assigning a cut-off distance and selecting core points. Therefore, this article improves the density peak clustering method from two aspects. First, the Gaussian kernel is substituted with a k-nearest neighbors method to calculate local density. This is important as compared with selecting a cut-off distance, calculating the k-value is easier. Second, the core points are automatically selected, unlike the original method that manually selects the core points regarding local density and distance distribution. Given that users' selection influences the clustering result, the proposed automatic core point selection strategy overcomes the human interference problem. Additionally, in the clustering process, the proposed method reduces the influence of manually assigned parameters.     

一种Dijkstra算法改进方法的研究与实现

从路径冗余角度研究了传统Dijkstra算法中的“交会路径”和“循环路径”问题,并针对上述问题提出了一种Dijkstra算法改进方法.该方法优化了路径搜索空间,降低了原算法的时间复杂度和空间复杂度,提高了算法的运行效率.最后,通过实验对该改进算法进行了验证.     

地球化学的空间自相关异常信息提取方法

针对地球化学数据存在的空间分布相关性特征,该文提出了一种基于空间自相关统计的地球化学异常提取方法。以内蒙古浩布高矿床外围的土壤地球化学数据为例,通过对Sn、Cu元素地球化学数据在不同空间间隔上的全局自相关计算,测算其空间聚集的程度,选取聚集程度最高时的间隔距离作为局部空间自相关的参数,通过局部Moran’s I值研究元素的空间分布特征,分析空间聚类和异常值,从而提取地球化学异常。结果表明,局部空间自相关分析可以揭示Sn、Cu地球化学数据的空间分布特征,能够更好地提取地球化学弱缓异常,说明空间自相关是一种有效的地球化学异常识别方法。     

一种基于改进OTSU评价函数的图像分割方法

O tsu阈值分割法是目前应用较为广泛的分割方法,其原理简单,计算量小,性能稳定。本文基于其类间方差和类内方差两个评价函数,在连续域内利用微积分原理进行了改进推广,实现了两个评价函数公式的统一,进而在满足文中函数的同时也满足了两个评价函数的要求,给出了快速实现算法。最后,通过实例对比证明,该方法自动选取的阈值合理,极大提高了图像分割的运算效率。     

Development of an inversion algorithm for dry snow density estimation and its application with ENVISAT-ASAR dual co-polarization data

Radar remote sensing has great potential to determine the extent and properties of snow cover. Availability of space-borne sensor dual-polarization C-band data of environmental satellite- (ENVISAT-) advanced synthetic aperture radar (ASAR) can enhance the accuracy in measurement of snow physical parameters as compared with single polarization data measurement. This study shows the capability of C-band synthetic aperture radar (SAR) data for estimating dry snow density over snow covered rugged terrain in Himalayan region. The snow density is an important parameter for the snow hydrology and avalanche forecasting related studies. An algorithm has been developed for estimating snow density, based on snow volume scattering and snow-ground scattering components. The radar backscattering coefficients of both horizontal–horizontal (hh) and vertical–vertical (vv) polarization and incidence angle are used as inputs in the algorithm to provide the snow dielectric constant which can be used to derive snow density using Looyenga's semi-empirical formula. Comparison was made between snow density estimated from algorithm using ENVISAT-ASAR hh and vv polarization data and the measured field value. The mean absolute error between estimated and measured snow density was found to be 0.024 g/cm³.     

Detecting clusters of disease with R

One of the main concerns of Public Health surveillance is the detection of clusters of disease, i. e., the presence of high incidence rates around a particular location, which usually means a higher risk of suffering from the disease under study (Aylin et al. 1999). Many methods have been proposed for cluster detection, ranging from visual inspection of disease maps to full Bayesian models analysed using MCMC. In this paper we describe the use and implementation, as a package for the R programming language, of several methods which have been widely used in the literature, such as Openshaws GAM, Stones test and others. Although some of the statistics involved in these methods have an asymptotical distribution, bootstrap will be used to estimate their actual sampling distributions.We would like to thank co-editor Dr. Manfred M. Fischer and four anonymous referees for their suggestions and comments to improve this paper. The help of Dr. Roger Bivand has also been of great value. Furthermore, this work has been partly funded by Consellería de Sanitat and EUROHEIS Project (code SI2.329122, 2001CVG2-604). The authors wish to express their regard and gratitude to Prof. Juan Ferrándiz-Ferragud who died during the revision of this paper. Juan was the main researcher of the Spanish EUROHEIS group, and was really a master for all the people involved in the project.     

一种改进的等高线断点连接方法

介绍了几种现有的等高线断点连接方法,并在最小点对法的基础上提出了一种改进的断点连接方法。此方法在判断断点间距离大小之前先对等高线进行预处理,即将各条目标等高线自动赋予不同的颜色值,然后将图幅进行格网划分,在不同网格内搜索断点,根据颜色的差异及距离的远近自动进行等高线连接。此方法连接的准确率高于一般的断点连接方法。