基于GIS技术的莱州湾东岸河流分形研究

应用地理信息系统(GIS)技术提取了莱州湾东岸河流信息,在此基础上运用计盒方法对莱州湾东岸的黄水河和王河水系进行了分形分析,获得了两水系的主河道河长分维数和河网分维数,并以此探讨了分维数与流域地貌和地质构造之间的相互关系。分形分析为研究区流域地貌学与水文学研究提供了一种新的研究途经。     

规则格网DEM化简的一种改进方法

分析了从较高分辨率DEM化简到较低分辨率DEM的地形简化方法的优缺点,提出了考虑整体地形特征信息的一种改进方法,实现了改进后的方法具有较好的简化效果。     

基于等高线的Multigen Creator地形建模

Multigen Creator具有强大的地物建模功能,但不同领域的建模仍缺乏统一的方法,应结合领域特点与应用需求进行具体方法设计。本文以某露天采石场的3维建模与可视化为例,探讨了基于等高线建立3维地面模型实现可视化的方法,介绍了其在某采石场的应用,模拟开采后的整个生态地貌,为政府决策以及绿色开采提供了有力的技术支持。     

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。     

自适应双层网格的空间索引研究

空间数据索引是GIS系统中的关键技术,针对目前主流的网格空间索引方法进行了充分的研究比较,指出了网格索引方法对空间要素进行索引是高效的,但对于非规则地图它是不适应的.在此基础上设计了一种新的空间索引的方法,即自适应双层网格索引.详细地介绍了这种模型的实现过程,并且用实验充分论证了这个模型的效率,认为利用自适应双层网格索引检索空间数据时,处理、查询效率和占用空间等各方面具有较大优势.     

GNSS-R DDM波形仿真的时延与多普勒间隔研究

DDM波形是GNSS-R技术用于反演地球物理参量的基本观测量,其仿真结果的可靠性直接影响GNSS-R的理论研究及项目工程参数设计。利用GREEPS仿真软件分析了时延间隔与多普勒间隔对DDM波形仿真结果可靠性的影响,得到了用于获取可靠DDM仿真波形的时延与多普勒间隔参数。研究表明,时延和多普勒间隔越小,DDM仿真波形与理论波形吻合度越高;当时延间隔取1/16个GPS L1 C/A码元、多普勒间隔取50 Hz时,DDM仿真波形与理论波形几乎完全吻合,二者的相关系数大于0.99;时延间隔对DDM仿真波形峰值位置的影响远大于对峰值信噪比的影响;而多普勒间隔对DDM仿真波形峰值位置与信噪比的影响相当。     

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.     

3D SLAM的室内背包移动测量系统研究

针对室内移动测图中GPS信号缺失,导致无法获取精确定位坐标与导航的问题,该文研发了一套室内背包式移动测量系统,该系统能够利用三维激光定位与测图的方法实现快速同步定位及地图创建和室内三维激光点云获取,可以应用在室内或地下的环境中进行数据采集,在不损失作业效率的前提下获取系统作业轨迹、高精度点云等多种数据结果。为了对系统的精度进行验证,使用该系统在实验室大楼走廊中进行实验获取室内三维激光点云,并将实验结果与采用常规测量手段得到的结果进行对比分析。实验表明,此系统的相对精度和绝对精度分别能够达到0.048和0.047m,在室内三维信息获取过程中相比较于传统作业方式显著提高了作业效率和数据质量,能够满足室内建模与测图要求。