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[转载]【雷达与对抗】【2017.03】半城市环境下谱激光雷达分析与地形分类

已有 336 次阅读 2019-10-13 17:16 |系统分类:科研笔记|文章来源:转载

本文为美国海军研究生院(作者:Charles A. McIver)的硕士论文,共181页。

 

对海军研究生院校园进行了遥感分析,包括建筑物、不透水表面(沥青和混凝土)、天然地面和植被。数据来自Optech Titan扫描仪,10-15/平方米的速度提供三波长激光数据53210641550纳米)。激光扫描仪(LiDAR)数据的分析技术传统上只使用XYZ坐标信息。传统方法用于将分类过程初始化为广泛的空间类别(未分类、地面、植被和建筑物)。谱分析为分类过程提供了一种独特的方法。

 

设计用于多光谱图像的工具和技术以适于本文中的激光雷达分析。ENVIN维可视化工具被用来开发监督分类技术的训练集,主要是最大似然方法。对组合空间/谱数据的无监督分类使用K均值分类器进行比较。校园被划分为10 - 16个类别,相比之下,传统的方法有4个。增加光谱成分可以改善不透水表面、其他地面元素和建筑材料之间的区别。最大似然度显示总体分类准确度为75%,包括草地(99.9%)、草皮(95%)、沥青瓦(94%)、轻质建筑混凝土(89%)、沙子(88%)、灌木(85%)、沥青(84%)、树木(80%)和粘土瓦(77%)。通过“返回数”进行后处理过滤,总体精度可以提高到82%

 

Remote-sensing analysis is conducted forthe Naval Postgraduate School campus, containing buildings, impervious surfaces(asphalt and concrete), natural ground, and vegetation. Data are from the OptechTitan scanner, providing three-wavelength laser data (532, 1064, and 1550 nm)at 10-15 points/m2 . Analysis techniques for laser-scanner (LiDAR) datatraditionally use only x, y, z coordinate information. The traditional approachis used to initialize the classification process into broad-spatial classes(unclassified, ground, vegetation, and buildings). Spectral analysiscontributes a unique approach to the classification process. Tools andtechniques designed for multispectral imagery are adapted to the LiDAR analysisherein. ENVI’s N-Dimensional Visualizer is employed to develop training setsfor supervised classification techniques, primarily Maximum Likelihood.Unsupervised classification for the combined spatial/spectral data isaccomplished using a K-means classifier for comparison. The campus isclassified into 10-16 classes, compared to the four from traditional methods.Addition of the spectral component improves the discrimination among impervioussurfaces, other ground elements, and building materials. Maximum Likelihooddemonstrates 75% overall classification accuracy, with grass (99.9%), turf(95%), asphalt shingles (94%), light-building concrete (89%), sand (88%),shrubs (85%), asphalt (84%), trees (80%), and clay-tile shingles (77%). Post-processfiltering by ‘number of returns’ increases overall accuracy to 82%.

 

引言

星载激光雷达系统的历史与文献综述

数据集与准备工作

处理方法

分类分析与结果

结论

附录 基础数据处理:NASA数据集衍生图、点云和数字高程模型


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