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	LiDAR Data Classification Using Spatial Transformation and CNN
	He, Xin1; Wang, Aili1; Ghamisi, Pedram2; Li, Guoyu3; Chen, Yushi4
刊名	IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
	2019
卷号	16期号:1页码:125-129
关键词	Convolutional neural networks (CNNs) deep learning feature extraction light detection and ranging (LiDAR) morphological profile (MP) spatial transformation network (STN)
ISSN号	1545-598X
DOI	10.1109/LGRS.2018.2868378
通讯作者	Chen, Yushi(chenyushi@hit.edu.cn)
英文摘要	Light detection and ranging (LiDAR) is a useful data acquisition technique, which is widely used in a variety of practical applications. The classification of LiDAR-derived rasterized digital surface model (LiDAR-DSM) is a fundamental technique in LiDAR data processing. In recent years, deep learning methods, especially convolutional neural networks (CNNs), have shown their capability in remote sensing areas, including LiDAR data processing. Traditional deep models empirically use a fixed neighborhood system as input to the network. Therefore, the weight and height of the input rectangle may not be optimal. In order to modify such handcrafted setting, a spatial transformation network is used here to identify optimal inputs. The transformed inputs are fed into a well-designed CNN to obtain the final classification results. Furthermore, morphological profiles are combined with spatial transformation CNN to further improve the classification accuracy. The proposed frameworks are tested on two LiDAR-DSMs (i.e., the Recology and Houston data sets). The experimental results show that the proposed models provide competitive results compared to the state-of-the-art methods. Furthermore, the proposed optimal input identification approach can also be found beneficial for other remote sensing applications.
收录类别	SCI
WOS关键词	DEEP
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Geochemistry & Geophysics ; Engineering, Electrical & Electronic ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000455181800026
内容类型	期刊论文
URI标识	http://www.corc.org.cn/handle/1471x/2558204
专题	寒区旱区环境与工程研究所
通讯作者	Chen, Yushi
作者单位	1.Harbin Univ Sci & Technol, Higher Educ Key Lab Measure & Control Technol & I, Harbin 150080, Heilongjiang, Peoples R China 2.Helmholtz Zentrum Dresden Rossendorf, Helmholtz Inst Freiberg Resource Technol Explorat, D-09599 Freiberg, Germany 3.Chinese Acad Sci, State Key Lab Frozen Soil Engn, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Gansu, Peoples R China 4.Harbin Inst Technol, Sch Elect & Informat Engn, Harbin 150001, Heilongjiang, Peoples R China
推荐引用方式 GB/T 7714	He, Xin,Wang, Aili,Ghamisi, Pedram,et al. LiDAR Data Classification Using Spatial Transformation and CNN[J]. IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,2019,16(1):125-129.
APA	He, Xin,Wang, Aili,Ghamisi, Pedram,Li, Guoyu,&Chen, Yushi.(2019).LiDAR Data Classification Using Spatial Transformation and CNN.IEEE GEOSCIENCE AND REMOTE SENSING LETTERS,16(1),125-129.
MLA	He, Xin,et al."LiDAR Data Classification Using Spatial Transformation and CNN".IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 16.1(2019):125-129.

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