应用Supernatural GIS进行3维城市数据分区管理的方法研究

探讨了3维城市数据管理的有关问题,基于3维GIS软件Supernatural GIS研究了3维城市模型大场景海量数据的分区管理、动态调度和快速显示问题,提出了应用Supernatural GIS实现城市3维可视化及大范围城市景观动态漫游的解决方案。     

一种基于人工免疫的图像分割算法

图像二维熵分割,一直因耗时长而限制了实际应用。本文借鉴生物免疫思想,提出二维熵图像分割的人工免疫算法。在克隆选择算法中引入疫苗的免疫接种,用于优化最优分割阈值对的搜索过程。在遥感高分辨率图像上的实验显示,该算法不仅能准确搜索到最优阈值对,而且计算时间只有传统算法的1.8%。该算法也验证了人工免疫思想用于图像分割的可行性和有效性。     

An improved building detection approach using L-band POLSAR two-dimensional time-frequency decomposition over oriented built-up areas

The objective of this study is to efficiently extract detailed information about various man-made targets in oriented built-up areas using polarimetric synthetic aperture radar (POLSAR) images. This paper develops an improved approach for building detection by utilizing Two-Dimensional Time-Frequency (2-D TF) decomposition. This method performs outstandingly in distinguishing between man-made and natural targets based on the isotropic behaviors, frequency-sensitive responses, and scattering mechanisms of objects. The proposed method can preserve the spatial resolution and exploit the advantages of TF decomposition; specifically, the exact outlines of buildings can be effectively located, and more types of features (e.g., flat roofs, roads, and walls that are oblique to the radar illumination) can be distinguished from forests in complex built-up areas by 2-D TF decomposition. The coarser-resolution subaperture images that are produced in the azimuth direction, which correspond to different looking angles, are beneficial for detecting man-made structures with main scattering centers oriented at oblique angles with respect to the radar illumination. In the range direction, the obtained subaperture images, which correspond to various observation frequencies, can be helpful in distinguishing flat roofs and roads from forests. This method was successfully implemented to analyze both NASA/JPL L-band AIRSAR and L-band EMISAR data sets. The building detection results of the proposed method exhibit a significant improvement over those of other methods and reach an overall accuracy over 80%, with approximately 20% higher than the accuracies of K-means clustering and the entropy/alpha-Wishart classifier and approximately 10% higher than the accuracy of the support vector machine method. Moreover, building details can be precisely detected, obliquely oriented buildings can be identified, and the distinction between buildings and forests is significantly improved, as both visually and statistically indicated. This method is highly adaptable and has substantial application value.     

Building a model-based personalised recommendation approach for tourist attractions from geotagged social media data

When travelling, people are accustomed to taking and uploading photos on social media websites, which has led to the accumulation of huge numbers of geotagged photos. Combined with multisource information (e.g. weather, transportation, or textual information), these geotagged photos could help us in constructing user preference profiles at a high level of detail. Therefore, using these geotagged photos, we built a personalised recommendation system to provide attraction recommendations that match a user's preferences. Specifically, we retrieved a geotagged photo collection from the public API for Flickr (Flickr.com) and fetched a large amount of other contextual information to rebuild a user's travel history. We then created a model-based recommendation method with a two-stage architecture that consists of candidate generation (the matching process) and candidate ranking. In the matching process, we used a support vector machine model that was modified for multiclass classification to generate the candidate list. In addition, we used a gradient boosting regression tree to score each candidate and rerank the list. Finally, we evaluated our recommendation results with respect to accuracy and ranking ability. Compared with widely used memory-based methods, our proposed method performs significantly better in the cold-start situation and when mining ‘long-tail’ data.     

荆江河道GPS与全站仪边长及方向改化

在区域范围较大的工程测量中,需对方向和边长观测值作归化改正和投影改正,但这一点常被许多测绘工作者忽略,因而造成不能对工程测量的结果加以正确的处理,甚至怀疑观测精度不够,还需加以复测,这给工程的进度造成了不良影响。以在荆江河段河道演变监测控制测量中对方向和边长观测值作归化改正和投影改正为例,阐述了边长及方向归化改正的具体方法及改化过程,通过改化计算,同时也说明全站仪导线边长测量成果经改化后能满足后续工程勘测精度的要求。     

利用卫星轨道资料获取降水云团尺度的新方案

提出一种新的统计方案,以目标面积比为核心度量指标,并参照连续圆形目标定义目标面积比意义上的等效半径,实现对目标宏观尺度大小的量化估计。此方法能有效减轻轨道截断效应所造成的目标尺度低估,可对通过计数类方法得到的目标空间尺度做出有效修正。     

基于模糊推理的空间聚类方法研究

提出了基于模糊推理的空间聚类方法,给出了其实现步骤,并以实例验证了其可行性和科学性。     

土地利用总体规划信息系统设计与实现

针对当前土地利用总体规划数据来源复杂、专业性强和工作效率低等问题,结合GIS、数据库、网络和.NET技术,从总体功能、数据库和组网环境设计出发,利用组件式二次开发方式实现了土地利用总体规划信息系统。该系统能够基本实现土地利用总体规划的自动化、规范化和信息化,较大地提高了土地利用总体规划工作效率。     

自动化监测系统在深基坑监测中的可靠性分析

深基坑监测对于城市工程的安全建设具有重要的指导意义。为了探究自动化监测系统在城市深基坑监测中的可行性,根据全站仪的测量原理利用Matlab进行三维仿真,从理论上证明了自动化监测系统的平面精度能够达到规范要求。采用尺度变形分析及水平位移对比分析,论证了在城市深基坑监测中利用自动化监测系统进行水平位移监测的可靠性。     

Contribution of new AAM data source to δLOD excitation

Data sets of the changes of the length of day (ΔLOD) measured by space geodetic techniques and of the atmospheric angular momentum (AAM) derived from global meteorological data by the National Meteorological Center (NMC) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used to reanalyze and study the excitations of ΔLOD, and to examine and compare the contribution of each AAM series to ΔLOD. The main results are as follows. 1. The AAM reanalyzed with the assimilated global meteorological data by NCEP/NCAR are more accurate and have lower noise than the original AAM derived by NMC. The NCEP/NCAR-based AAM is more consistent with the changes of the length-of-day series. 2. The NCEP reanalysed AAM data may better explain the non-tidal LOD variations on intraseasonal to interannual time scales, especially on the quasi-biennial time scale. The atmosphere cannot, however, explain all LOD variations; other excitation sources are possible. 3. The effects of atmosphere on the estimated values of tides for ΔLOD components up to a month are weak. The preliminary results of the annual and semiannual tides can be estimated after removing the effects of atmosphere from ΔLOD. Received: 27 May 1998 / Accepted: 22 March 1999