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 conferencePaper

1 Citation (Scopus)

Abstract

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
Pages2879-2882
Number of pages4
DOIs
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

Conference(2017) IEEE International Symposium on Geoscience and Remote Sensing IGARSS.
Abbreviated titleIGARSS 2017
CountryUnited States
CityFort Worth
Period23/07/1728/07/17

Keywords

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

Cite this

Liao, W., Vancoillie, F., Li, L., Zhao, B., Gao, L., Philips, W., & Zhang, B. (2017). Fusion of multi-scale hyperspectral and LiDAR features for tree species mapping. 2879-2882. Paper presented at (2017) IEEE International Symposium on Geoscience and Remote Sensing IGARSS., Fort Worth, United States. https://doi.org/10.1109/IGARSS.2017.8127599