结合MST划分和RHMRF-FCM算法的高分辨率遥感图像分割

针对基于像素的HMRF-FCM算法抗噪性差以及对地物复杂边界分割精度低的问题,提出一种结合形状信息的静态MST区域划分和RHMRF-FCM算法的高分辨率遥感图像分割方法。该方法定义一种静态MST同质区域划分准则,借助MST能较好表达边界和形状信息、能较好抑制几何噪声的特点,解决地物复杂边界的表达和降低分割结果中几何噪声问题。首先,利用MST静态划分将图像域划分成若干个均质区域,假设每个均质区域内光谱测度服从独立同一的多元高斯分布。然后,在此基础上构建了区域隐马尔可夫随机场模型,以及建立基于信息熵和KL信息正则化项的模糊聚类目标函数。最后,采用偏微分方法对分割模型参数进行求解,从而得到全局最优分割结果。为验证本文方法,对WorldView-3高分遥感图像进行分割试验。定性、定量分析了尺度参数、光谱相似性参数和区域紧致度参数对最优分割结果的影响,并对比分析本文算法和eCognition软件中的多分辨率分割算法、分水岭算法。     

面向双/多变量的连续面域拓扑图可视化方法

面域拓扑图是一种利用区域面积大小定量表达区域属性信息的可视化方法。由于其区域面积本身已经表示某一变量,因此这更有利于双/多变量的制图表达。针对目前基于面域拓扑图的双/多变量表达方法中存在的难以表达相邻区域之间基本状况和不利于不同地理现象的空间分布规律及差异表达的问题,本文提出一种面向双/多变量的连续面域拓扑图可视化方法。首先通过格网密度补偿和积分步长逐步试探的方法对基于扩散模型的连续面域拓扑图生成算法进行部分优化,完成基本变量的表达,然后分别通过空间内插和符号扩展完成第2和第3变量在连续面域拓扑图中的表达。最后以慕尼黑市人口密度和银行/ATM分布(双变量)数据以及奥格斯堡市人口密度数据、幼儿园分布以及规模数据(多变量)为试验数据进行可视化,并通过实证分析验证了该方法的有效性和优越性。     

对基础测绘"十四五"规划的若干思考

着眼于明确机构改革后基础测绘在自然资源管理业务格局中的职责定位、工作重点,以及如何按照新《测绘法》更好地履行"为经济建设、国防建设、社会发展和生态保护服务"的法定要求,分析了"十四五"基础测绘发展的基本形势,据此提出"十四五"规划工作的基本思路和主要遵循,明确了工作重点和相关措施,相关观点已经在"十四五"基础测绘规划编制工作中得到体现。     

国家应急测绘调度系统设计

通过分析应急测绘指挥调度的基本特点与实际需求,结合国家应急标准规范,采用基于多通道多协议的数据通信与传输技术、基于MPP架构的分布式数据库管理技术、基于UML模型化的任务规划技术,根据无人机、多功能方舱等应急装备实时回传的位置、状态和应急测绘地理信息,以及快速处理与共享系统等产生的数据成果与作业信息,设计完成了具有应急调度方案制定、指令快速下达、测绘装备动态调度与实时跟踪、测绘任务进度监控及应急任务演练等功能的应急测绘调度系统。     

兼顾志愿者地理信息的国家应急测绘公众服务平台设计

随着互联网技术的进步和新一代网络信息传播模式的深入发展,志愿者地理信息的研究和应用日益广泛,但由于其规范性和均一性方面的缺陷,在应急公共服务领域,志愿者信息的有效利用依旧是亟需突破的难题。本文将重点介绍国家应急测绘公众服务平台设计思路和总体架构,并结合志愿者地理信息特点,探讨该类信息在国家应急测绘公众服务平台建设中的处理流程和应用模式,力求在应急信息资源共享和服务平台共建方面进行新的尝试,为我国突发事件应急体系建设提供有益实践。     

无人机机载LiDAR在沿海滩涂大比例尺地形测绘中的应用

以南京市以下长江12.5 m深水航道建设工程中的白茆沙整治建筑物工程的年度监测为例,介绍了利用无人机航摄和机载LiDAR技术进行大比例尺高精度的滩涂地理信息数据采集的关键技术。研究案例表明,采用无人机搭载数码相机和LiDAR联合进行数据采集,在小区域、通行困难地区的高分辨率影像和地面高程数据快速获取方面具有明显优势,为沿海滩涂开发中的工程用图测绘工作提供了一种新的有效快捷的技术手段。     

一体化综合减灾智能服务系统研究

针对目前我国减灾服务重决策轻预警,减灾服务技术、系统、应用集成不够,尚缺乏完善的一体化综合减灾智能服务体系现状。该文从测绘地理信息视角,分析了室内外一体化应急定位、多源应急数据融合、灾害场景可视化、灾害模型分析服务等关键技术,构建了一体化综合减灾智能服务原型系统,并在国家基础地理信息中心和陕西省的相关应急部门进行了实际应用,实现了应急信息的一体化感知、定位、融合、分析、服务,为我国综合减灾决策分析提供了技术支撑。     

The use of cartographic control points in the harmonization and revision of MRDBs

The main purpose of the research is to achieve fully automated approach for supplying multi-resolution databases with linear objects in each scale. Moreover, the proposed solutions maintain the repeatability and accuracy of output data wherever possible according to the input scale. These properties are achieved by keeping the minimal object dimensions as well as the appropriate data pre-processing, based on the classification of source points. The classification distinguishes three classes of points: constant (unchangeable), temporary, and inherited. These classes build a structure of cartographic control points. Based on these solutions, the authors proposed an algorithm for linear object simplification based on minimal object dimensions and cartographic control points. It was also confirmed that the simplification between constant points does not cause geometry discrepancies in relation to the global simplification of the whole line.     

Towards new applications of underwater photogrammetry for investigating coral reef morphology and habitat complexity in the Myeik Archipelago,Myanmar

Photogrammetry represents a non-destructive, cost-effective tool for coral reef monitoring, able to integrate traditional remote sensing techniques and support researchers’ work. However, its application to submerged habitats is still in early stage. We present new ways to employ Structure from Motion techniques to infer properties of reef habitats. In particular, we propose the use of Digital Surface Models and Digital Terrain Models for assessing coral colonies extension and height and discriminating between seabed and coral cover. Such information can be coupled with digital rugosity estimates to improve habitat characterization. DTM, DSM and orthophotos were derived and used to compute a series of metrics like coral morphologies, reef topography, coral cover and structural complexity. We show the potentialities offered by underwater photogrammetry and derived products to provide useful basic information for marine habitat mapping, opening the possibility to extend these methods for large-scale assessment and monitoring of coral reefs.     

Geospatial data mining for digital raster mapping

We performed an in-depth literature survey to identify the most popular data mining approaches that have been applied for raster mapping of ecological parameters through the use of Geographic Information Systems (GIS) and remotely sensed data. Popular data mining approaches included decision trees or “data mining” trees which consist of regression and classification trees, random forests, neural networks, and support vector machines. The advantages of each data mining approach as well as approaches to avoid overfitting are subsequently discussed. We also provide suggestions and examples for the mapping of problematic variables or classes, future or historical projections, and avoidance of model bias. Finally, we address the separate issues of parallel processing, error mapping, and incorporation of “no data” values into modeling processes. Given the improved availability of digital spatial products and remote sensing products, data mining approaches combined with parallel processing potentials should greatly improve the quality and extent of ecological datasets.