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Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion

Renbo Luo (UGent) , Wenzhi Liao (UGent) , Hongyan Zhang (UGent) , Youguo Pi and Wilfried Philips (UGent)
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Abstract
Hyperspectral and LiDAR data, can provide plentiful information about the objects on the Earths surface. However there are some shortages for each of them, where hyperspectral sensor is easily influenced by cloud and difficult to distinguish different objects contained same materials, LiDAR cannot discriminate different objects which are similar in altitude. Fusion of these multi-source data for reliable classification attracts increasing interests but remains challenging. In this paper, we propose a new framework to fuse multi-source data for classification. The proposed method contains three main works: 1) cloud shadows extraction; 2) feature fusion of spectral and spatial information extracted from hyperspectral image, elevation information extracted from LiDAR data; 3) decision fusion of cloud and non-cloud regions. Experimental results on real HSI and LiDAR data demonstrate effectiveness of the proposed method both visually and quantitatively.
Keywords
hyperspectral image, LiDAR data, Remote sensing, feature fusion

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MLA
Luo, Renbo, et al. “Classification of Cloudy Hyperspectral Image and Lidar Data Based on Feature and Decision Fusion.” 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), edited by Ji WU et al., IEEE, 2016, pp. 2518–21.
APA
Luo, R., Liao, W., Zhang, H., Pi, Y., & Philips, W. (2016). Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion. In J. WU, Y. JIN, & J. SHI (Eds.), 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) (pp. 2518–2521). Beijing: IEEE.
Chicago author-date
Luo, Renbo, Wenzhi Liao, Hongyan Zhang, Youguo Pi, and Wilfried Philips. 2016. “Classification of Cloudy Hyperspectral Image and Lidar Data Based on Feature and Decision Fusion.” In 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), edited by Ji WU, Yaqiu JIN, and Jiancheng SHI, 2518–21. IEEE.
Chicago author-date (all authors)
Luo, Renbo, Wenzhi Liao, Hongyan Zhang, Youguo Pi, and Wilfried Philips. 2016. “Classification of Cloudy Hyperspectral Image and Lidar Data Based on Feature and Decision Fusion.” In 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), ed by. Ji WU, Yaqiu JIN, and Jiancheng SHI, 2518–2521. IEEE.
Vancouver
1.
Luo R, Liao W, Zhang H, Pi Y, Philips W. Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion. In: WU J, JIN Y, SHI J, editors. 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS). IEEE; 2016. p. 2518–21.
IEEE
[1]
R. Luo, W. Liao, H. Zhang, Y. Pi, and W. Philips, “Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion,” in 2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), Beijing, 2016, pp. 2518–2521.
@inproceedings{7181948,
  abstract     = {{Hyperspectral and LiDAR data, can provide plentiful information about the objects on the Earths surface. However there are some shortages for each of them, where hyperspectral sensor is easily influenced by cloud and difficult to distinguish different objects contained same materials, LiDAR cannot discriminate different objects which are similar in altitude. Fusion of these multi-source data for reliable classification attracts increasing interests but remains challenging. In this paper, we propose a new framework to fuse multi-source data for classification. The proposed method contains three main works: 1) cloud shadows extraction; 2) feature fusion of spectral and spatial information extracted from hyperspectral image, elevation information extracted from LiDAR data; 3)
decision fusion of cloud and non-cloud regions. Experimental
results on real HSI and LiDAR data demonstrate effectiveness
of the proposed method both visually and quantitatively.}},
  author       = {{Luo, Renbo and Liao, Wenzhi and Zhang, Hongyan and Pi, Youguo and Philips, Wilfried}},
  booktitle    = {{2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)}},
  editor       = {{WU, Ji and JIN, Yaqiu and SHI, Jiancheng}},
  isbn         = {{978-1-5090-3332-4}},
  issn         = {{2153-6996}},
  keywords     = {{hyperspectral image,LiDAR data,Remote sensing,feature fusion}},
  language     = {{eng}},
  location     = {{Beijing}},
  pages        = {{2518--2521}},
  publisher    = {{IEEE}},
  title        = {{Classification of cloudy hyperspectral image and lidar data based on feature and decision fusion}},
  year         = {{2016}},
}

Web of Science
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