山脊线和山谷线自动提取的一种新方法

针对当前山脊线和山谷线的提取都是从其几何特征或物理特征的单一方面进行研究和设计,不能够利用隐含在数字化资料中的山脊线和山谷线的有效信息的弊端;本文利用几何分析方法Douglas-Peuker提取山脊线和山谷线的特征点,并结合提取区域内地形概略的分水线和汇水线,用概略的分水线和汇水线对特征点进行识别、归类、顺序提取各条山脊线和山谷线,把山脊线的几何特性与物理特性结合起来,克服了各自的弊端。实践表明提取的山脊线和山谷线与实际基本符合。     

一种微地形探测回波信号处理的新算法

针对钴结壳具体赋存环境和开采技术要求,作者提出了利用Morlet小波信号包络和高斯函数曲线及能量相关性算法对拟设计的微地形探测方法接收到的超声回波信号进行分析与计算。由实验初步验证,利用该算法计算出来的被测对象位置精度及误差稳定性均高于传统的超声波单波束多探头探测方法。     

一种新的全球海量地形实时绘制算法

本文为实现全球海量地形数据的实时可视化,提出了一种新算法。算法不使用几何数据而是利用球面特征进行地形多分辨率模型初建,然后基于视锥与节点关系对初建结果进行扩展来得到完整的地形网格。此外设计了能消除具有复杂邻接关系的节点间裂缝的拼接方式,构造了简洁的方法消除GPU32位浮点精度导致的"wob-bling"现象。实现的算法在普通微机上平均漫游速度达每秒95帧以上。     

Context-based automatic reconstruction and texturing of 3D urban terrain for quick-response tasks

Highly detailed 3D urban terrain models are the base for quick response tasks with indispensable human participation, e.g., disaster management. Thus, it is important to automate and accelerate the process of urban terrain modeling from sensor data such that the resulting 3D model is semantic, compact, recognizable, and easily usable for training and simulation purposes. To provide essential geometric attributes, buildings and trees must be identified among elevated objects in digital surface models. After building ground-plan estimation and roof details analysis, images from oblique airborne imagery are used to cover building faces with up-to-date texture thus achieving a better recognizability of the model. The three steps of the texturing procedure are sensor pose estimation, assessment of polygons projected into the images, and texture synthesis. Free geographic data, providing additional information about streets, forest areas, and other topographic object types, suppress false alarms and enrich the reconstruction results.     

A wavelet-extreme learning machine for low-cost INS/GPS navigation system in high-speed applications

The combined navigation system consisting of both global positioning system (GPS) and inertial navigation system (INS) results in reliable, accurate, and continuous navigation capability when compared to either a GPS or an INS stand-alone system. To improve the overall performance of low-cost micro-electro-mechanical systems (MEMS)-based INS/GPS by considering a high level of stochastic noise on low-cost MEMS-based inertial sensors, a highly complex problems with noisy real data, a high-speed vehicle, and GPS signal outage during our experiments, we suggest two approaches at different steps: (1) improving the signal-to-noise ratio of the inertial sensor measurements and attenuating high-frequency noise using the discrete wavelet transform technique before data fusion while preserving important information like the vehicle motion information and (2) enhancing the positioning accuracy and speed by an extreme learning machine (ELM) which has the characteristics of quick learning speed and impressive generalization performance. We present a single-hidden layer feedforward neural network which is employed to optimize the estimation accuracy and speed by minimizing the error, especially in the high-speed vehicle and real-time implementation applications. To validate the performance of our proposed method, the results are compared with an adaptive neuro-fuzzy inference system (ANFIS) and an extended Kalman filter (EKF) method. The achieved accuracies are discussed. The results suggest a promising and superior prospect for ELM in the field of positioning for low-cost MEMS-based inertial sensors in the absence of GPS signal, as it outperforms ANFIS and EKF by approximately 50 and 70%, respectively.     

顾及测区地形的水准网定权新方法研究

针对目前水准网平差中高差定权方法未能较好地考虑地形环境对高差观测误差的影响关系的问题,提出一种顾及测区地形起伏并根据测段往返测高差较差的水准网高差分类定权新方法。理论分析和实际数据证明,在水准网中既有地形起伏较大的测段,又有地形起伏不大的平缓测段时,采用文中提出的定权方法比传统定权方法更能真实反映水准网测量的精度情况,弥补了传统定权方法的不足。     

面向地上下无缝集成建模的新一代三维地理信息系统

地上下无缝集成三维建模是新一代3DGIS的主要标志,地上下无缝集成建模已成为当务之急。在三维空间建模技术现状与存在问题、目标层次与功能需求及三维空间模型现状与趋势分析的基础上,介绍了地上下集成建模的两个层次和无缝集成的基本原理;阐述以CD-TIN为纽带、以BRep-TIN-GTP为核心、以三层混合模型为成份的地上下集成空间数据模型的概念结构与典型逻辑关系。并结合城市与矿山应用,介绍该集成模型在GeoMo3D系统中初步实现后的可视化效果。同时,指出了与地上下集成建模相关的近年3DGIS的主要攻关方向和重点理论难题。     

Improving settlement selection for small-scale maps using data enrichment and machine learning

Acquiring and formalizing cartographic knowledge still is a challenge, especially when the generalization process concerns small-scale maps. We concentrate on the settlement selection process for small-scale maps, with the aim of rendering it more holistic, and making methodological contributions in four areas. First, we show how written specifications and rules can be validated against the actual published map products, thus pointing to gaps and potential improvements. Second, we use data enrichment based on supplementing information extracted from point-of-interest data in order to assign functional importance to particular settlements. Third, we use machine learning (ML) algorithms to infer additional rules from existing maps, thus making explicit the deep knowledge of cartographers and allowing to extend the cartographic rule set. And fourth, we show how the results of ML can be transformed into human-readable form for potential use in the guidelines of national mapping agencies. We use the case of settlement selection in the small-scale maps published by the Polish national mapping agency (GUGiK). However, we believe that the methods and findings of this paper can be adapted to other environments with minor modifications.     

A support vector machine classifier based on a new kernel function model for hyperspectral data

The kernel function is a key factor to determine the performance of a support vector machine (SVM) classifier. Choosing and constructing appropriate kernel function models has been a hot topic in SVM studies. But so far, its implementation can only rely on the experience and the specific sample characteristics without a unified pattern. Thus, this article explored the related theories and research findings of kernel functions, analyzed the classification characteristics of EO-1 Hyperion hyperspectral imagery, and combined a polynomial kernel function with a radial basis kernel function to form a new kernel function model (PRBF). Then, a hyperspectral remote sensing imagery classifier was constructed based on the PRBF model, and a genetic algorithm (GA) was used to optimize the SVM parameters. On the basis of theoretical analysis, this article completed object classification experiments on the Hyperion hyperspectral imagery of experimental areas and verified the high classification accuracy of the model. The experimental results show that the effect of hyperspectral image classification based on this PRBF model is apparently better than the model established by a single global or local kernel function and thus can greatly improve the accuracy of object identification and classification. The highest overall classification accuracy and kappa coefficient reached 93.246% and 0.907, respectively, in all experiments.     

地形特征提取的一种简易算法

地形特征作为基本地理要素,从DEM数据中提取其隐含的地形结构信息是非常重要的。本方法采用距离倒数加权法建立格网DEM,提出一种直接比较高程值来提取地形特征的简易算法,并利用差分算法计算坡度和坡向,通过V isual C++软件平台,编程实现格网DEM地形特征的自动提取,进行三维立体显示。该算法易于编程实现,数据结构简单,程序运行效率高,提取结果与真实地形基本拟合。     

Towards intelligent geospatial data discovery: a machine learning framework for search ranking

Current search engines in most geospatial data portals tend to induce users to focus on one single-data characteristic dimension (e.g. popularity and release date). This approach largely fails to take account of users’ multidimensional preferences for geospatial data, and hence may likely result in a less than optimal user experience in discovering the most applicable dataset. This study reports a machine learning framework to address the ranking challenge, the fundamental obstacle in geospatial data discovery, by (1) identifying a number of ranking features of geospatial data to represent users’ multidimensional preferences by considering semantics, user behavior, spatial similarity, and static dataset metadata attributes; (2) applying a machine learning method to automatically learn a ranking function; and (3) proposing a system architecture to combine existing search-oriented open source software, semantic knowledge base, ranking feature extraction, and machine learning algorithm. Results show that the machine learning approach outperforms other methods, in terms of both precision at K and normalized discounted cumulative gain. As an early attempt of utilizing machine learning to improve the search ranking in the geospatial domain, we expect this work to set an example for further research and open the door towards intelligent geospatial data discovery.     

Optimizing support vector machine learning for semi-arid vegetation mapping by using clustering analysis

In remote sensing communities, support vector machine (SVM) learning has recently received increasing attention. SVM learning usually requires large memory and enormous amounts of computation time on large training sets. According to SVM algorithms, the SVM classification decision function is fully determined by support vectors, which compose a subset of the training sets. In this regard, a solution to optimize SVM learning is to efficiently reduce training sets. In this paper, a data reduction method based on agglomerative hierarchical clustering is proposed to obtain smaller training sets for SVM learning. Using a multiple angle remote sensing dataset of a semi-arid region, the effectiveness of the proposed method is evaluated by classification experiments with a series of reduced training sets. The experiments show that there is no loss of SVM accuracy when the original training set is reduced to 34% using the proposed approach. Maximum likelihood classification (MLC) also is applied on the reduced training sets. The results show that MLC can also maintain the classification accuracy. This implies that the most informative data instances can be retained by this approach.     

机器学习法在面向对象影像分类中的对比分析

针对如何选择遥感影像面向地理对象分类方法的问题,该文面向地理国情普查中的地表覆盖分类应用,以3个典型区域(山区、平原、城区)的多源高分辨率遥感影像为实验数据,从分类效果、分类精度等方面对比分析3种分类方法(支持向量机、决策树、随机森林)的优劣。在相同影像分割、特征提取、样本采集条件下,通过333组分类实验,得出以下规律:支持向量机分类方法稳定性强,分类速度快,但对特征数的要求高,特征数目与总体精度、地物环境之间的规律性不强,从而增加了特征提取与选择的难度,而随着特征的增加,决策树、随机森林的总体分类精度均为先升高后降低,最后趋于平衡。最后,综合随机森林对特征的优选机制和支持向量机的高分类精度,得到新的组合分类器。     

一种顾及地形复杂度的LiDAR点云多尺度滤波方法

针对复杂地形区域的机载LiDAR数据滤波方法中自适应阈值设置问题,根据地形多尺度效应,提出一种自适应阈值的机载LiDAR点云多尺度滤波方法。该方法采用影像金字塔策略按分辨率从高至低逐级构建LiDAR点云分层格网,滤波过程则从最大尺度格网(顶层格网,最低分辨率)开始,采用局部统计分析的方法自适应地确定高差阈值,同时结合薄板样条内插出下层各格网控制点的高程值,直至最底层格网完成原始激光点云滤波。通过我国某山区城市复杂地形的LiDAR数据实验表明顾及地形复杂度的LiDAR点云多尺度滤波方法能够快速有效地提取高精度DEM,能够满足实际生产需求。     

A formula for gravimetric terrain corrections using powers of topographic height

Journal of Geodesy - ?The application of Stokes' formula to create geoid undulations requires no masses outside the geoid. However, due to the existence of the topography, terrain...     

地形TIN中地理实体表达与LOD技术

现有的LOD技术确保了3维地表的实时绘制,但其不能解决以地理实体为结构进行组织、具有实体表达能力的高精度、大规模地形TIN的3维可视化实时响应问题,仍经常发生场景滞涩、跳跃等现象。为此,本文从面向对象的角度出发,对地形TIN按实体进行组织,建立了OTIN,设计出实体包围球、高程误差队列等结构,可以确保以实体为单位的大规模地形TIN的实时绘制。     

Combining machine learning and ontological data handling for multi-source classification of nature conservation areas

Manual field surveys for nature conservation management are expensive and time-consuming and could be supplemented and streamlined by using Remote Sensing (RS). RS is critical to meet requirements of existing laws such as the EU Habitats Directive (HabDir) and more importantly to meet future challenges. The full potential of RS has yet to be harnessed as different nomenclatures and procedures hinder interoperability, comparison and provenance. Therefore, automated tools are needed to use RS data to produce comparable, empirical data outputs that lend themselves to data discovery and provenance. These issues are addressed by a novel, semi-automatic ontology-based classification method that uses machine learning algorithms and Web Ontology Language (OWL) ontologies that yields traceable, interoperable and observation-based classification outputs. The method was tested on European Union Nature Information System (EUNIS) grasslands in Rheinland-Palatinate, Germany. The developed methodology is a first step in developing observation-based ontologies in the field of nature conservation. The tests show promising results for the determination of the grassland indicators wetness and alkalinity with an overall accuracy of 85% for alkalinity and 76% for wetness.     

基于机器学习的多施工参数盾构施工姿态预测

针对盾构施工过程中参数易变、轴线难以预测控制的问题,本文提出了基于机器学习的多施工参数盾构施工姿态预测方法,分析了在复杂环境下影响盾构姿态掘进施工的参数,以及掘进参数和盾构姿态内在的关联关系,建立了盾构姿态预测模型,实现了盾首盾尾中心坐标与设计轴线的偏差计算;最后结合某地铁施工段,验证了该预测模型的可行性.     

外存大规模地形实时动态绘制算法

本文结合视点相关的层次细节技术、可见性剔除技术以及多线程技术等,提出一种基于外存中大规模地形实时动态绘制算法。在将原始DEM数据分割成多块标准化地形块的前提下,调度线程根据视景体顶点移动情况预测后续绘制所需地形并将其从外存预先加载到内存;绘制线程主要借助动态四叉树结构、标识矩阵、裁剪视景体来实现地形的多分辨率网格模型快速生成。实验表明,算法能够在普通PC机上实现大规模地形的实时绘制和漫游。