• Laser & Optoelectronics Progress
  • Vol. 53, Issue 8, 82802 (2016)
Zhang Aiwu*, Xiao Tao, and Duan Yihao
Author Affiliations
  • [in Chinese]
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    DOI: 10.3788/lop53.082802 Cite this Article Set citation alerts
    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): 82802 Copy Citation Text show less
    References

    [1] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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] 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.

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    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): 82802
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