基于机载LiDAR和Mean Shift算法提取森林密度

陈伟; 杨敏华; 洪奕丰; 李飞

doi:10.3969/j.issn.0258-2724.2015.06.026

基于机载LiDAR和Mean Shift算法提取森林密度

doi: 10.3969/j.issn.0258-2724.2015.06.026

基金项目:

中国科学院战略性先导科技专项资助项目(XDA05050100)

详细信息

作者简介:
陈伟(1986-),男,博士研究生,研究方向为机载Lidar森林生物量估算,E-mail:lajiao.1225@163.com

计量
- 文章访问数: 588
- HTML全文浏览量: 63
- PDF下载量: 294
- 被引次数: 0
出版历程
- 收稿日期: 2014-06-14
- 刊出日期: 2015-12-25

Extraction of Forest Density Based on Airborne LiDAR and Mean Shift Algorithms

摘要

摘要: 为获取森林密度信息,利用Mean Shift算法对森林点云进行单木分割提取森林密度信息.首先,以点云三维坐标和法向量作为特征向量,利用统计分析方法选择合适带宽及阈值,采用Mean Shift算法对点云进行初始分割;其次,对分割后的点云进行分析,加入灌木、杂草等过滤条件,得到树冠点云;然后,对树冠点云再次进行Mean Shift分割,并对每类树冠点云进行统计,以稳态点为粗略位置标记计算森林密度;最后,与地面实测数据进行验证.地面数据验证结果表明,平均计算精度达到90.0%以上,可满足林业应用需求;通过与分水岭法进行对比发现, Mean Shift方法获得的精度为92.5%,比分水岭方法70.0%高出22.5%,且避免了分水岭方法导致的过分割现象.
- 森林密度 /
- Mean Shift算法 /
- LiDAR /
- 分水岭法 /
- 特征向量
Abstract: To extract forest density, Mean Shift segmentation algorithm is explored for point clouds processing. The first step is to use the Mean Shift algorithm for the point clouds initial segmentation depended on feature vectors, bandwidth and thresholds. Feature vectors consist of three-dimensional coordinates and normal vector of the point clouds and bandwidth and thresholds are determined by statistical analysis method. The second step is to obtain canopy point clouds through the analysis of the segmented point clouds added with filtering conditions such as shrubs and weeds. The third step is to use Mean Shift algorithm to calculate forest density. With the statistical analysis of segmented the canopy point clouds in each category, the steady state points are marked as the rough locations for individual trees. Finally, the results are verified by field measured data. It is found that the average accuracy of this method could be more than 90.0%, which meets the requirements of forestry industry. In comparison with the Watershed method, the accuracy of the Mean Shift method is 92.5%, which is higher than Watershed method with 70.0% accuracy, and it can avoid the over segmentation in the Watershed method.
- forest density /
- Mean Shift algorithm /
- LiDAR /
- Watershed method /
- feature vectors

HTML全文

参考文献(18)

庞勇,赵峰,李增元,等. 机载激光雷达平均树高提取研究[J]. 遥感学报,2008,12(1): 152-158. PANG Yong, ZHAO Feng, LI Zengyuan, et al. Forest height inversion using airborne lidar technology[J]. Journal of Remote Sensing, 2008, 12(1): 152-158.

吴振亚,王明辉,张瑞平,等. 一种基于ADS-B的雷达误差实时融合校正算法[J]. 西南交通大学学报,2013,48(1): 102-106. WU Zhenya, WANG Minghui, ZHANG Ruiping, et al. ADS-B-based algorithm for real-time optimal estimation of radar biases[J]. Journal of Southwest Jiaotong University, 2013, 48(1): 102-106.

刘清旺. 机载激光雷达森林参数估测方法研究[D]. 北京:中国林业科学研究院,2009.

赵旦. 基于激光雷达和高光谱遥感的森林单木关键参数提取[D]. 北京:中国林业科学研究院,2012.

CHEN Q. Isolating individual trees in a savanna woodland using small footprint LiDAR data[J]. Photogrammetric Engineering Remote Sensing, 2006, 72(8): 923-932.

LI W, GUO Q, JAKUBOWSKI M K, et al. A new method for segmenting individual trees from the lidar point cloud[J]. Photogrammetric Engineering Remote Sensing, 2012, 78(1): 75-84.

WELNACKER H, HEYDER U, KOCH B. Detection of individual tree crowns in airborne LiDAR data[J]. Photogrammetric Engineering Remote Sensing, 2006, 72(4): 357-362.

COMANICIU D, RAMESH V, MEER P. Real-time tracking of non-rigid objects using Mean Shift[C]// Proceedings of IEEE Conference on Computer Vision Pattern Recognition. : IEEE, 2000: 142-149.

LIU K Y, SUN S Q, LIU S Y, et al. Target tracking Mean-Shift algorithm based on improved color feature and histogram updating[J]. Journal of Transportation Systems Engineering Information Technology, 2012, 12(4): 84-92.

MELZER T. Non-parametric segmentation of ALS point clouds using Mean Shift[J]. Journal of Applied Geodesy Jag, 2008, 1(3): 159-170.

REITBERGER J, SCHNRR C, KRZYSTEK P, et al. 3D segmentation of single trees exploiting full waveform LIDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2009, 64(6): 561-574.

COMANICIU D, MEER P. Mean Shift: a robust approach toward feature space analysis[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2002, 24(5): 603-619.

曲巨宝. 基于智能Agent的车辆自适应跟踪技术[J]. 西南交通大学学报,2011,46(4): 626-632. QU Jubao. Adaptive technology for vehicles tracking based on intelligent Agent[J]. Journal of Southwest Jiaotong University, 2011, 46(4): 626-632.

周家香,朱建军,梅小明,等. 多维特征自适应Mean Shift遥感图像分割方法[J]. 武汉大学学报:信息科学版,2012,37(4): 419-422. ZHOU Jiaxiang, ZHU Jianjun, MEI Xiaoming, et al. An adaptive Mean Shift segmentation method of remote sensing images based on multi-dimension features[J]. Geomatics and Information Science of Wuhan University, 2012, 37(4): 419-422.

孙正林,邹峥嵘,吴爱琴. 一种改进的Mean Shift点云数据滤波[J]. 测绘工程,2011,20(5): 57-59. SUN Zhenglin, ZOU Zhengrong, WU Aiqin. An improved Mean Shift algorithm used for point cloud data filtering[J]. Engineering of Surveying and Mapping, 2011, 20(5): 57-59.

LIU C, ZHANG Y. Extraction of 3D feature from LiDAR data fused with aerial images using improved Mean-Shift algorithm[J]. Survey Review, 2011, 43(13): 402-414.

FERRAZ A, BRETAR F, JACQUEMOUD S, et al. 3-D mapping of a multi-layered Mediterranean forest using ALS data[J]. Remote Sensing of Environment, 2012, 121(138): 210-223.

SHU K B O, FENG P D I, HUA W L I, et al. Mean Shift based clustering analysis of multi-spectral remote sensing imagery[J]. International Journal of Remote Sensing, 2006, 30(4): 817-827.

施引文献

附加材料(0)

访问统计

点击查看大图

计量

文章访问数: 588
HTML全文浏览量: 63
PDF下载量: 294
被引次数: 0

基于机载LiDAR和Mean Shift算法提取森林密度

doi: 10.3969/j.issn.0258-2724.2015.06.026

作者简介: 陈伟(1986-),男,博士研究生,研究方向为机载Lidar森林生物量估算,E-mail:lajiao.1225@163.com

计量

出版历程

Extraction of Forest Density Based on Airborne LiDAR and Mean Shift Algorithms

计量

出版历程

目录

作者简介:
陈伟(1986-),男,博士研究生,研究方向为机载Lidar森林生物量估算,E-mail:lajiao.1225@163.com