激光与光电子学进展, 2016, 53 (8): 082802, 网络出版: 2016-08-11   

一种机载LiDAR点云分类的自适应特征选择方法 下载: 531次

A Method of Adaptive Feature Selection for Airborne LiDAR Point Cloud Classification
作者单位
首都师范大学三维信息获取与应用教育部重点实验室, 北京 100048
引用该论文

张爱武, 肖涛, 段乙好. 一种机载LiDAR点云分类的自适应特征选择方法[J]. 激光与光电子学进展, 2016, 53(8): 082802.

Zhang Aiwu, Xiao Tao, Duan Yihao. A Method of Adaptive Feature Selection for Airborne LiDAR Point Cloud Classification[J]. Laser & Optoelectronics Progress, 2016, 53(8): 082802.

参考文献

[1] 左志权, 张祖勋, 张剑清. 区域回波比率与拓扑识别模型结合的城区激光雷达点云分类方法[J]. 中国激光, 2012, 39(4): 0414001.

    Zuo Zhiquan, Zhang Zuxun, Zhang Jianqing. Classification of LiDAR point clouds for urban area based on multi-echo region ratio and recognition topology model[J]. Chinese J Lasers, 2012, 39(4): 0414001.

[2] 张志伟, 刘志刚. 利用既有知识渐近数学形态学提取LiDAR数据中道路信息方法研究[J]. 测绘科学, 2010, 35(4): 154-156.

    Zhang Zhiwei, Liu Zhigang. Method extraction road information in LIDAR data based on the existing knowledge of asymptotic mathematical morphology[J]. Science of Surveying and Mapping, 2010, 35(4): 154-156.

[3] 卢维欣, 万幼川, 何培培, 等. 大场景内建筑物点云提取及平面分割算法[J]. 中国激光, 2015, 42(9): 0914004.

    Lu Weixin, Wan Youchun, He Peipei, et al. Extracting and plane segmenting building from large scene point cloud[J]. Chinese J Lasers, 2015, 42(9): 0914004.

[4] 苗启广, 郭雪, 宋建锋, 等. 基于区域预测的LiDAR点云数据形态学滤波算法[J]. 激光与光电子学进展, 2015, 52(1): 011003.

    Miao Qiguang, Guo Xue, Song Jianfeng, et al. LiDAR point cloud dta with morphological filter algorithm based on region prediction[J]. Laser & Optoelectronics Progress, 2015, 52(1): 011003.

[5] Strmbu V F, Strmbu B M. A graph-based segmentation algorithm for tree crown extraction using airborne LiDAR data[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 104: 30-43.

[6] 龚亮, 李正国, 包全福. 融合航空影像的LiDAR地物点云分类[J]. 测绘工程, 2012, 21(1): 34-38.

    Gong Liang, Li Zhengguo, Bao Quanfu. Classifition of LiDAR object points by fusing aerial image[J]. Engineering of Surveying and Mapping, 2012, 21(1): 34-38.

[7] Gong L, Zhang Y, Li Z, et al. Automated road extraction from LiDAR data based on intensity and aerial photo[C]. 2010 3rd International Congress on Image and Signal Processing (CISP), 2010: 2130-2133.

[8] 刘丽娟, 庞勇, 范文义, 等. 机载LiDAR和高光谱融合实现温带天然林树种识别[J]. 遥感学报, 2013, 17(3): 679-695.

    Liu Lijuan, Pang Yong, Fan Wenyi, et al. Fused airborne LiDAR and hyperspectral data for tree species identification in a natural temperate forest[J]. Journal of Remote Sensing, 2013, 17(3): 679-695.

[9] Mallet C, Bretar F, Roux M, et al. Relevance assessment of full-waveform lidar data for urban area classification[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(6): S71-S84.

[10] Kononenko I. Estimating attributes: Analysis and extensions of RELIEF[J]. Machine Learning, 2005, 784: 171-182.

[11] Chen Y W, Lin C J. Combining SVMs with various feature selection strategies[M]. Berlin: Springer, 2006: 315-324.

[12] Guo L, Chehata N, Mallet C, et al. Relevance of airborne lidar and multispectral image data for urban scene classification using random forests[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(1): 56-66.

[13] 孙杰, 赖祖龙. 利用随机森林的城区机载LiDAR数据特征选择与分类[J]. 武汉大学学报·信息科学版, 2014, 11(39): 1310-1313.

    Sun Jie, Lai Zulong. Airborne LiDAR feature selectiong for urban classification using random forests[J]. Geomatics and Information Science of Wuhan University, 2014, 39(11): 1310-1313.

[14] 郭波, 黄先锋, 张帆, 等. 顾及空间上下文关系的Joint Boost点云分类及特征降维[J]. 测绘学报, 2014, 42(5): 715-821.

    Guo Bo, Huang Xianfeng, Zhang Fan, et al. Points cloud classification using jointboost combined with contextual information for feature reduction[J]. Acta Geodaetica et Cartographica Sinica, 2013, 42(5): 715-721.

[15] Torralba A, Murphy K P, Freeman W T. Sharing visual features for multiclass and multiview object detection[J]. Pattern Analysis and Machine Intelligence, 2007, 29(5): 854-869.

[16] 范士俊, 张爱武, 胡少兴, 等. 基于随机森林的机载激光全波形点云数据分类方法[J]. 中国激光, 2013, 40(9): 0914001.

    Fan Shijun, Zhang Aiwu, Hu Shaoxin, et al. A method of classification for airborne full waveform LiDAR data based on random forest[J]. Chinese J Lasers, 2013, 40(9): 0914001.

[17] 孟放. 大型三维点云数据的交互绘制研究[D]. 北京: 北京大学, 2005.

    Meng Fang. The study of interactive rendering for large scale three dimesional point cloud data[D]. Beijing: Beijing University, 2005.

[18] Carlberg M, Gao P, Chen G, et al. Classifying urban landscape in aerial LiDAR using 3D shape analysis[C]. Image Processing (ICIP), 2009: 1681-1684.

[19] Gross H, Thoennessen U. Extraction of lines from laser point clouds[C]. Symposium of ISPRS Commission III: Photogrammetric Computer Vision PCV06 International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 2006: 86-91.

[20] 李含伦, 张爱武, 刘诏, 等. 基于LiDAR波形分解的点云SVM分类方法研究[J]. 测绘通报, 2014, 1: 28-32.

    Li Hanlun, Zhang Aiwu, Liu Zhao, et al. A LiDAR point classification method based on SVM and waveform decomposition[J]. Bulletin of Surveying and Mapping, 2014, 1: 28-32.

[21] Breiman L. Random forests[J]. Machine Learning, 2001, 45(1): 5-32.

[22] 姚登举, 杨静, 詹晓娟. 基于随机森林的特征选择算法[J]. 吉林大学学报(工学版), 2014, 44(1): 137-141.

    Yao Dengju, Yang Jing, Zhan Xiaojuan. Feature selection algorithm based on random forest[J]. Journal of Jilin Univerdity (Engineering and Technology Edition), 2014, 44(1): 137-141.

[23] Verikas A, Gelzinis A, Bacauskiene M. Mining data with random forests: A survey and results of new tests[J]. Pattern Recognition, 2011, 44(2): 330-349.

[24] Vapnik V N. Statistical learning theory[M]. New York: Wiley, 1998.

[25] Luts J, Ojeda F, van de Plas D, et al. A tutorial on support vector machine-based methods for classification problems in chemometrics[J]. Analytica Chimica Acta, 2010, 665(2): 129-145.

张爱武, 肖涛, 段乙好. 一种机载LiDAR点云分类的自适应特征选择方法[J]. 激光与光电子学进展, 2016, 53(8): 082802. Zhang Aiwu, Xiao Tao, Duan Yihao. A Method of Adaptive Feature Selection for Airborne LiDAR Point Cloud Classification[J]. Laser & Optoelectronics Progress, 2016, 53(8): 082802.

本文已被 8 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!