Analysis of spatial clustering optimization     

Jianfeng Yang  Puliu Yan  Delin Xia  Qing Geng 《地球空间信息科学学报》2013,16(4):302-307

Spatial clustering is widely used in many fields such as WSN (Wireless Sensor Networks), web clustering, remote sensing and so on for discovery groups and to identify interesting distributions in the underlying database. By discussing the relationships between the optimal clustering and the initial seeds, a clustering validity index and the principle of seeking initial seeds were proposed, and on this principle we recommend an initial seed-seeking strategy: SSPG (Single-Shortest-Path Graph). With SSPG strategy used in clustering algorithms, we find that the result of clustering is optimized with more probability. At the end of the paper, according to the combinational theory of optimization, a method is proposed to obtain optimal reference k value of cluster number, and is proven to be efficient.  相似文献   

Model generalization of two different drainage patterns by self-organizing maps     

Alper Sen  Turkay Gokgoz  Monika Sester 《制图学和地理信息科学》2013,40(2):151-165

In this study, we develop a new method using self-organizing maps (SOMs) for the selection of hydrographic model generalization. The most suitable attributes of the stream objects are used as input variables to the SOM. The attributes were weighted using Pearson’s chi-square independence test. We used the Radical Law to determine how many features should be selected, and an incremental approach was developed to determine which clusters should be selected from the SOM. Two drainage patterns (dendritic and modified basic) were obtained from the National Hydrography Datasets of United States Geological Survey at 1:24,000-scale (high resolution) and used in order to derive stream networks at 1:100,000-scale (medium resolution). The 1:100,000-scale stream networks, derived in accordance with the proposed approach, are similar to those in the original maps in both quantity and visual aspects. Stream density and pattern were maintained in each subunit, and continuous and semantically correct networks were obtained.  相似文献   

Identification of Convective and Stratiform Clouds Based on the Improved DBSCAN Clustering Algorithm     

Yuanyuan ZUO  Zhiqun HU  Shujie YUAN  Jiafeng ZHENG  Xiaoyan YIN  Boyong LI 《大气科学进展》2022,39(12):2203-2212

A convective and stratiform cloud classification method for weather radar is proposed based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm. To identify convective and stratiform clouds in different developmental phases, two-dimensional (2D) and three-dimensional (3D) models are proposed by applying reflectivity factors at 0.5° and at 0.5°, 1.5°, and 2.4° elevation angles, respectively. According to the thresholds of the algorithm, which include echo intensity, the echo top height of 35 dBZ (ET), density threshold, and ε neighborhood, cloud clusters can be marked into four types: deep-convective cloud (DCC), shallow-convective cloud (SCC), hybrid convective-stratiform cloud (HCS), and stratiform cloud (SFC) types. Each cloud cluster type is further identified as a core area and boundary area, which can provide more abundant cloud structure information. The algorithm is verified using the volume scan data observed with new-generation S-band weather radars in Nanjing, Xuzhou, and Qingdao. The results show that cloud clusters can be intuitively identified as core and boundary points, which change in area continuously during the process of convective evolution, by the improved DBSCAN algorithm. Therefore, the occurrence and disappearance of convective weather can be estimated in advance by observing the changes of the classification. Because density thresholds are different and multiple elevations are utilized in the 3D model, the identified echo types and areas are dissimilar between the 2D and 3D models. The 3D model identifies larger convective and stratiform clouds than the 2D model. However, the developing convective clouds of small areas at lower heights cannot be identified with the 3D model because they are covered by thick stratiform clouds. In addition, the 3D model can avoid the influence of the melting layer and better suggest convective clouds in the developmental stage.  相似文献   

基于聚类算法的钻孔瞬变电磁视电阻率立体成像方法     

范涛  李鸿泰  刘磊  赵睿  李博凡  郭建磊  李宇腾 《地球科学与环境学报》2021,(2):343-355

煤矿井下地球物理水害超前探测要求探测点20 m范围内不得有积水和金属物体,传统电磁法超前探测技术已不能满足要求,钻孔瞬变电磁法通过将收发装置送入掘进工作面前方的钻孔中进行探测,既远离了巷道中的各种干扰,又提高了隐蔽致灾水体的探测精度。为解决该方法对钻孔径向异常体的准确定位解释难题,通过三维正演总结了其水平分量异常响应特征,提出了异常体象限确定准则,研究了根据水平分量幅值和异常象限综合求取异常体工具面角的计算方法。将由垂直分量计算得到的每一个视电阻率视为独立异常体,基于K-means聚类算法对相应的水平分量异常曲线特征值进行二分类,实现了全数据集的视电阻率象限自动划分,结合异常工具面角算法研究得出钻孔瞬变电磁视电阻率立体成像方法。最后计算了三维数值模型的立体成像结果,对钻孔径向的小规模低阻异常体取得了良好效果。结果表明:基于K-means聚类算法的钻孔瞬变电磁视电阻率立体成像方法是地球物理与机器学习的有机结合,该方法能够为井下掘进工作面隐伏水害超前探测精细解释提供技术支撑。  相似文献   

应用改进欧拉算法解算磁性目标空间位置参数     

卞光浪  翟国君  刘雁春  黄谟涛  欧阳永忠 《测绘学报》2011,40(3):386-392

指出目前磁性目标探测中常规欧拉方法存在的局限性,提出在欧拉窗口内视地磁场和其他目标干扰异常联合影响为线性变化,将构造指数作为动态变化参数代入欧拉方程一并求解,并利用多元线性回归方法解决了欧拉方程非线性问题.在质量控制方案中,根据构造指数和目标深度变化规律,给出离散欧拉解滤波措施;采用层次聚类分析方法对滤波后欧拉解进行分...  相似文献   

时空一体化框架下时空异常探测   总被引：1，自引：0，他引：1  

刘启亮  邓敏  王佳璆  彭思岭  梅小明  赵玲 《遥感学报》2011,15(3):457-474

：提出一种时空一体化的时空异常探测方法,首先基于时空统计学与聚类分析构建一体化时空邻近域。进而, 发展兼顾时空相关与异质性的时空异常度量方法。最后,采用一种3步骤的策略探测时空异常。应用本文方法探测中国 陆地区域33年(1970年—2002年)的年平均气温时空数据中的时空异常,探测结果具有较好的可靠性,反映时空数据的时 空一体化特征。同时,对时空异常的产生机理与实际意义进行分析和解释。  相似文献   

基于力学思想的空间聚类有效性评价     

刘启亮  邓敏  彭东亮  王佳璆 《武汉大学学报(信息科学版)》2011,(8):982-986,990

从力学的角度来考虑空间聚类问题,并结合地理学基本规律提出了一种基于力学思想的空间聚类有效性评价指标(简称SCV)。实验分析表明,本文提出的评价指标能够更准确、高效地对二维地理空间数据的硬聚类结果进行有效性评价。  相似文献   

一种利用K均值和SOM进行遥感图像分类的方法     

张涛 《地理空间信息》2011,9(1):109-111

探讨了一种将K均值算法和SOM神经网络算法相结合的方法,并将其应用于多光谱遥感图像分类,通过与K均值算法、ISODATA算法和SOM算法的对比实验,验证了该方法的有效性.  相似文献   

密度聚类算法在连续分布点云去噪中的应用     

张巧英  陈浩  朱爽 《地理空间信息》2011,(6):101-104

在原始测量获取的点云数据中,除了目标数据外,还有大量的噪声数据。噪声往往无规律地分布在目标物体周围,难以用统一数学模型区分。基于密度的聚类算法将簇定义为密度相连的点的最大集合,能发现任意形状、大小的类簇,将该算法应用在点云去噪中,能将密度分布连续点进行聚类,从中提取出目标点云。  相似文献   

A Methodology for Clustering Lakes in Alberta on the basis of Water Quality Parameters     

Tahir Ali Akbar  Quazi K. Hassan  Gopal Achari 《洁净——土壤、空气、水》2011,39(10):916-924

In this study, a methodology for clustering 18 lakes in Alberta, Canada using the data of 19 water quality parameters for a period of 11 years (1988–2002) is presented. The methods consist of (i) principal component analysis (PCA) to determine the dominant water quality parameters, (ii) cluster analysis techniques to develop the characteristics of the clusters, and (iii) pattern‐match lakes to determine the appropriate cluster for each of the lakes. The PCA revealed that three principal components (PCs) were able to explain ～88% of the variability and the dominant water quality parameters were total dissolved solids, total phosphorus, and chlorophyll‐a. We obtained five clusters for the period 1994–1997 by using the dominant parameters with water quality deteriorating as the cluster number increased from 1 to 5. Upon matching cluster patterns with the entire dataset, it was observed that some of the lakes belonged to the same cluster all the time (e.g., cluster 1 for lakes Elkwater, Gregg, and Jarvis; cluster 3 for Sturgeon; cluster 4 for Moonshine; and cluster 5 for Saskatoon), while others changed with time. This methodology could be applied in other regions of the world to identify the most suitable source waters and prioritize their management. It could be helpful to analyze the natural controlling processes, pollution types, impact of seasonal changes and overall quality of source waters. This methodology could be used for monitoring water bodies in a cost effective and efficient way by sampling only less number of dominant parameters instead of using a large set of parameters.  相似文献