| 引用本文: | 周唯,彭认灿,董箭.LiDAR点云纹理特征提取方法.[J].国防科技大学学报,2019,41(2):124-131.[点击复制] |
| ZHOU Wei,PENG Rencan,DONG Jian.Method for extracting texture features of LiDAR point cloud[J].Journal of National University of Defense Technology,2019,41(2):124-131[点击复制] |
|
| |
|
|
| 本文已被：浏览 7685次 下载 5641次 |
| LiDAR点云纹理特征提取方法 |
| 周唯1,2,3, 彭认灿2,3, 董箭2,3 |
|
(1.中国人民解放军91550部队， 辽宁 大连 116023;2.海军大连舰艇学院 军事海洋测绘系， 辽宁 大连 116018;3.海军大连舰艇学院 海洋测绘工程军队重点实验室， 辽宁 大连 116018)
|
| 摘要: |
| 针对图像纹理应用于LiDAR点云分类过程中存在的多义性问题，提出点云纹理特征的概念。该特征属性反映了点与其邻域点的属性值分布情况，提取过程基于KD树数据检索结构和灰度共生矩阵算法。分析搜索邻域、移动步长和灰度等级等参数对点云纹理特征的影响，并利用支持向量机分类方法验证点云纹理特征，可以有效地辅助高程和强度信息以改善LiDAR点云的地物分类结果。实验还证明了相比于栅格格式的图像纹理特征，点云纹理特征约束的地物分类具有更高的分类精度，并且点云纹理特征在微小地物的甄别和水陆的区分方面具有突出的能力。该特征的这些优秀特性可以为海岸带机载LiDAR数据的精细化分类、海岸带高精度DEM构建和海岸线提取等工作发挥重要作用。 |
| 关键词: 机载激光雷达 点云纹理特征 灰度共生矩阵 地物分类 |
| DOI：10.11887/j.cn.201902018 |
| 投稿日期：2018-01-14 |
| 基金项目:国家重点基础研究发展计划资助项目（2017YFC1505505）；国家自然科学基金资助项目（41471380，41601498） |
|
| Method for extracting texture features of LiDAR point cloud |
| ZHOU Wei1,2,3, PENG Rencan2,3, DONG Jian2,3 |
|
(1.The PLA Unit 91550, Dalian 116023, China;2. Department of Military Oceanography and Hydrography & Cartography, Dalian Naval Academy, Dalian 116018, China;3.Key Laboratory of Hydrography and Cartography of PLA, Dalian Naval Academy, Dalian 116018, China)
|
| Abstract: |
| In order to eliminate the ambiguity in the land cover classification of LiDAR point cloud by using the image texture, the texture feature of point cloud based on the searching structure of KD tree and the gray level co-occurrence matrix were proposed, which represents the distribution of attribute values of points and their surrounding neighborhood points. The influence of the parameters, such as search neighborhood, moving step and gray-level, on the texture features of point cloud was analyzed. Using the support vector machine classification method, it was verified that the texture feature of point cloud can effectively assist the elevation and intensity to improve the results of the land cover classification. In addition, the results demonstrated that the land cover classification under the constraint of the texture features of point cloud has higher accuracy than that under the constraint of the raster based image texture features, and the texture features of point cloud perform outstandingly in distinguishing tiny land objects and separating the water and land. These excellent characteristics of the texture features of point cloud can contribute significantly to the refined classification of coastal LiDAR data, the construction of high precision DEM in coastal zone and the extraction of coastlines. |
| Keywords: airborne LiDAR texture features of point cloud gray-level co-occurrence matrix land cover classification |
|
|
|
|
|
