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

Renbo Luo, Wenzhi Liao, Hongyan Zhang, Youguo Pi, Wilfried Philips, Ji WU, Yaqiu JIN, Jiancheng SHI

Research output: Contribution to conferencePaper

2 Citations (Scopus)


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.
Original languageEnglish
Number of pages4
Publication statusPublished - 3 Nov 2016
Event2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) - Beijing, China
Duration: 10 Jul 201615 Jul 2016


Conference2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
Abbreviated titleIGARSS 2016


  • hyperspectral image
  • LiDAR data
  • remote sensing
  • feature fusion

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