Fusion of multi-scale hyperspectral and LiDAR features for tree species mapping

Wenzhi Liao, Frieke Vancoillie, Liwei Li, Bin Zhao, Lianru Gao, Wilfried Philips, Bing Zhang

Research output: Contribution to conferencePaperpeer-review

3 Citations (Scopus)


The added value of multiple data sources on tree species mapping has been widely analyzed. In particular, fusion of hyper-spectral (HS) and LiDAR sensors for forest applications is a very hot topic. In this paper, we exploit the use of multi-scale features to fuse HS and LiDAR data for tree species mapping. Hyperspectral data is obtained from the APEX sensor with 286 spectral bands. LiDAR data has been acquired with a TopoSys sensor Harrier 56 at full waveform. We generate multi-scale features on both HS and LiDAR data, by considering the diameter and the height layer of different tree species. Experimental results on a forested area in Belgium demonstrate the effectiveness of using multi-scale features for fusion of HS image and LiDAR data both visually and quantitatively.
Original languageEnglish
Number of pages4
Publication statusPublished - 4 Dec 2017
Event(2017) IEEE International Symposium on Geoscience and Remote Sensing IGARSS. - Fort Worth, United States
Duration: 23 Jul 201728 Jul 2017


Conference(2017) IEEE International Symposium on Geoscience and Remote Sensing IGARSS.
Abbreviated titleIGARSS 2017
Country/TerritoryUnited States
CityFort Worth


  • data fusion
  • remote sensing
  • hyperspectral image
  • LiDAR data
  • graph-based
  • classification
  • geophysical image processing
  • image classification

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