基于信息块法的矢量符号库的建立和符号化实现

基于试验成果,详细介绍了基于信息块法的矢量地图符号库的建立和空间信息的符号化。     

Tropical land use land cover mapping in Pará (Brazil) using discriminative Markov random fields and multi-temporal TerraSAR-X data

Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information. However, detection of short-term processes and land use patterns of high spatial–temporal variability is a challenging task.We present a novel framework using multi-temporal TerraSAR-X data and machine learning techniques, namely discriminative Markov random fields with spatio-temporal priors, and import vector machines, in order to advance the mapping of land cover characterized by short-term changes. Our study region covers a current deforestation frontier in the Brazilian state Pará with land cover dominated by primary forests, different types of pasture land and secondary vegetation, and land use dominated by short-term processes such as slash-and-burn activities. The data set comprises multi-temporal TerraSAR-X imagery acquired over the course of the 2014 dry season, as well as optical data (RapidEye, Landsat) for reference. Results show that land use land cover is reliably mapped, resulting in spatially adjusted overall accuracies of up to 79% in a five class setting, yet limitations for the differentiation of different pasture types remain.The proposed method is applicable on multi-temporal data sets, and constitutes a feasible approach to map land use land cover in regions that are affected by high-frequent temporal changes.     

基于EzMap的矢量专题图与影像专题图编制的比较研究

矢量专题图与影像专题图的区别在于是否具有影像内容以及矢量要素内容的选取和表示。本文基于Ez Map软件,从制图工艺流程、地理底图、专题要素内容、符号库设计、制图综合、表示方法、数据量和成图周期8个方面比较,详细讨论了矢量专题图与影像专题图编制的异同,指出了影像专题图的一些优势。     

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.     

Using snakes for the registration of topographic road database objects to ALS features

For historical reasons many national mapping agencies store their topographic data in a dual system consisting of a Digital Landscape Model (DLM) and a Digital Terrain Model (DTM). The DLM contains 2D vector data representing objects on the Earth’s surface, such as roads and rivers, whereas the DTM is a 2.5D representation of the related height information, often acquired by Airborne Laser Scanning (ALS). Today, many applications require reliable 3D topographic data. Therefore, it is advantageous to convert the dual system into a 3D DLM. However, as a result of different methods of acquisition, processing, and modelling, the registration of the two data sets often presents difficulties. Thus, a straightforward integration of the DTM and DLM might lead to inaccurate and semantically incorrect 3D objects.In this paper we propose a new method for the fusion of the two data sets that exploits parametric active contours (also called snakes), focusing on road networks. For that purpose, the roads from a DLM initialise the snakes, defining their topology and their internal energy, whereas ALS features exert external forces to the snake via the image energy. After the optimisation process the shape and position of the snakes should coincide with the ALS features. With respect to the robustness of the method several known modifications of snakes are combined in a consistent framework for DLM road network adaptation. One important modification redefines the standard internal energy and thus the geometrical model of the snake in order to prevent changes in shape or position not caused by significant features in the image energy. For this purpose, the initial shape is utilized creating template-like snakes with the ability of local adaptation. This is one crucial point towards the applicability of the entire method considering the strongly varying significance of the ALS features. Other concepts related to snakes are integrated which enable our method to model network and ribbon-like characteristics simultaneously. Additionally, besides ALS road features information about context objects, such as bridges and buildings, is introduced as part of the image energy to support the optimisation process. Meaningful examples are presented that emphasize and evaluate the applicability of the proposed method.     

基于改进PSO-SVM的噪声稳健建筑物变形监测方法

对高层、超高层建筑物进行实时,高精度的变形监测对提前预防安全隐患,保证人民生命财产安全具有重要意义.建筑物变形作为一种典型的随机性和微弱性过程,噪声等误差的存在会影响从中提取有用的变形信息.针对该问题,提出一种改进粒子群(ParticleSwarmOptimization,PSO)算法优化支持向量机(SupportVe...     

磁异常探测中磁场矢量导数快速计算方法

目的 针对计算磁位高阶导数常用的频率域快速傅立叶变换方法在实际应用中存在边界效应的问题,提出了分别在x和y方向上移相π/2二维余弦变换定义及其时域微分定理,推导出了磁场矢量分量的导数计算公式,并将其应用于方向解析信号的磁异常边界识别方法。通过棱柱体源磁场理论数据对比分析,验证了基于余弦变换计算磁位高阶导数的方法在不需要扩边或滤波处理、未增加额外处理数据和算法复杂度的前提下,可避免或减小边界效应,算法稳定性更好,计算结果精度更高。     

航空矢量重力测量水平分量的带限直接地形影响

基于第二类Helmert凝聚法的级数展开式,独立推导了航空矢量重力测量水平分量带限直接地形影响的计算公式。在中国西部山区选取了两条沿纬圈方向4000m航线高度上的航线,采用 的数字高程模型基于解析核计算了水平分量的直接地形影响,经低通滤波后与基于带限公式计算的地形影响进行了比较。数值计算结果表明,二者吻合较好,本文给出的带限地形影响公式可用于航空矢量重力测量水平分量的地形归算。     

基于多纹理和支持向量机的ZY-102C星HR数据分类

基于国产资源一号02C星高分辨率(high resolution,HR)影像,提取了基于变差函数、灰度共生矩阵和梯度的多纹理特征,结合光谱信息构建了基于支持向量机(support vector machine,SVM)的多源信息复合模型的图像分类方法,并与传统最大似然法和决策树法的分类结果进行了比较。研究表明,变差函数纹理和梯度纹理参与的多源复合数据有效提高了图像的分类精度,总分类精度由85.14%提高到87.43%,Kappa系数由0.82提高到0.85;绝对值变差函数为纹理最佳窗口分析提供了理论依据,基于累积步长提取的纹理特征能显著提升图像分类的精度,分类准确率提高了13.94%,Kappa系数增加了0.17;基于多源复合数据的SVM高空间分辨率遥感图像分类方法能有效解决传统图像分类结果破碎的问题,比最大似然方法和决策树法的分类精度显著提高,总精度达到89.14%,Kappa系数为0.87,分别提高了6.85%和10.84%。实验表明,ZY-102C星HR数据在冬小麦信息提取中具有一定的稳定性和优势。     

支持向量机的车载雷达点云目标识别

为了进一步研究移动测量系统的数据处理问题,该文根据点云的基本特征,归纳了由7个特征构成的点云原始特征向量,在此基础上,结合语义环境构建了由17个特征构成的点云扩展特征向量,并采用支持向量机模型对车载LiDAR点云进行行道树点云识别的一系列实验。实验中采用粒子群优化算法和遗传算法对支持向量机进行参数寻优;采用不同特征向量和不同数目样本对点云进行学习和目标识别;分析了特征向量的学习曲线和识别精度。实验结果表明,支持向量机模型能够在行道树点云识别中取得较高的精度。