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。     

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.     

Temporal replication of the national land cover database using active machine learning

Regional and national level land cover datasets, such as the National Land Cover Database (NLCD) in the United States, have become an important resource in physical and social science research. Updates to the NLCD have been conducted every 5 years since 2001; however, the procedure for producing a new release is labor-intensive and time-consuming, taking 3 or 4 years to complete. Furthermore, in most countries very few, if any, such releases exist, and thus there is high demand for efficient production of land cover data at different points in time. In this paper, an active machine learning framework for temporal updating (or backcasting) of land cover data is proposed and tested for three study sites covered by the NLCD. The approach employs a maximum entropy classifier to extract information from one Landsat image using the NLCD, and then replicate the classification on a Landsat image for the same geographic extent from a different point in time to create land cover data of similar quality. Results show that this framework can effectively replicate the land cover database in the temporal domain with similar levels of overall and within class agreement when compared against high resolution reference land cover datasets. These results demonstrate that the land cover information encapsulated in the NLCD can effectively be extracted using solely Landsat imagery for replication purposes. The algorithm is fully automated and scalable for applications at landscape and regional scales for multiple points in time.     

盾构机全站仪激光导向模式误差灵敏度分析

通过对盾构机全站仪激光导向系统的测量原理研究,并对盾构坐标与施工坐标系的旋转模型转换原理分析,建立盾构姿态参数与坐标旋转模型的关系.然后通过全站仪测量激光标靶棱镜和切口坐标与激光标靶相对位置的误差分析得到相关结论.     

高光谱影像概率分类向量机分类方法研究

从分析基于支持向量机和相关向量机的高光谱影像分类方法的优势和不足出发,将基于概率分类向量机的方法用于高光谱影像分类试验。在贝叶斯理论框架下,概率分类向量机为基函数权值引入截断Gauss先验概率分布,使得不同类别的基函数权值具有不同符号的先验分布,并利用EM算法进行参数推断,得到足够稀疏的概率模型,弥补了相关向量机选取错误类别的样本作为相关向量的不足,从而有效地提高了模型的分类精度和稳定性。OMIS和PHI影像分类试验表明,概率分类向量机能够很好地应用在高光谱影像分类。     

混合核特征加权SVM遥感湿地空间分类

针对湿地空间信息的复杂性和SVM的分类性能,设计一种基于混合核函数的特征加权SVM分类模型,综合利用多种特征信息,避免被弱相关特征所支配,从而提供更佳的映射性能和泛化能力。实验结果表明,该分类模型兼具良好的外推和内推能力,能够有效地融合不同信息源特征,得到更完整和准确的分类结果,在总体精度、Kappa系数等多项指标上都表现出更高的水平。     

改进支持向量机的高分遥感影像道路提取

针对支持向量机受分类数的限制在高分辨率遥感影像中无法直接获取高精度道路网信息的问题,该文提出一种新的混合的基于支持向量机的方法:首先,利用模糊C均值聚类方法将输入的遥感影像分为3类,以减少支持向量机的错分现象;其次,运用支持向量机将不同类别的像素分为道路类和非道路类;最后,应用马尔科夫随机场对分类结果进行噪声去除,并采用形态学进行后处理,进而得到精确道路网信息。实验结果表明:该算法不仅能够从高分辨率遥感影像中提取出道路网,而且精度优于直接使用支持向量机算法以及对比算法。     

一种裸露土壤湿度反演方法

针对目前土壤湿度反演方法研究较少且缺少实时性的现状,该文提出一种土壤湿度反演方法——最小二乘支持向量机技术。以积分方程模型为正向算法,数值模拟不同雷达参数(频率、入射角及极化)下后向散射系数随土壤含水量和地表粗糙度的变化情况。经过数据敏感性分析,选取C-波段和X-波段、小入射角下的同极化后向散射系数作为支持向量回归的训练样本信息;经过适当的训练,利用支持向量回归技术对土壤含水量进行了反演研究;并考虑通过多频率、多极化、多入射角数据的组合,消除地表粗糙度的影响,提高反演精度。模拟结果表明,该方法反演土壤湿度具有较高的精度和较好的实时性;同时,与人工神经网络方法的结果比较,证明了该方法的有效性,为土壤湿度的反演研究提供了一种方法。     

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...     

一种基于流形正则化的半监督指纹定位算法

针对传统指纹定位算法采集带标签训练数据成本高的问题,本文提出了一种基于流形正则化的半监督指纹定位算法。首先以流形假设为依据,利用批量输入的带标签数据与无标签数据之间的相似度构建图拉普拉斯算子;然后与极限学习机算法相结合,通过随机特征映射建立隐含层;最后在流形正则化框架下,求解隐含层和输出层之间的权值矩阵,从而建立位置估计模型。仿真结果表明,与INN、SVR、ELM 3种算法相比,该算法的训练和测试时间相对较短,且在带标签训练数据稀疏的前提下仍能保持较高的准确率与稳定性。