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

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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.
Keywords
Data fusion, remote sensing, hyperspectral image, LiDAR data, graph-based, CLASSIFICATION

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Chicago
Liao, Wenzhi, Frieke Vancoillie, Liwei Li, Bin Zhao, Lianru Gao, Wilfried Philips, and Bing Zhang. 2017. “Fusion of Multi-scale Hyperspectral and LiDAR Features for Tree Species Mapping.” In IEEE International Symposium on Geoscience and Remote Sensing IGARSS, 2879–2882. New York, NY, USA: IEEE.
APA
Liao, Wenzhi, 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. IEEE International Symposium on Geoscience and Remote Sensing IGARSS (pp. 2879–2882). Presented at the 2017 IEEE International Geoscience and Remote Sensing symposium (IGARSS), New York, NY, USA: IEEE.
Vancouver
1.
Liao W, Vancoillie F, Li L, Zhao B, Gao L, Philips W, et al. Fusion of multi-scale hyperspectral and LiDAR features for tree species mapping. IEEE International Symposium on Geoscience and Remote Sensing IGARSS. New York, NY, USA: IEEE; 2017. p. 2879–82.
MLA
Liao, Wenzhi, Frieke Vancoillie, Liwei Li, et al. “Fusion of Multi-scale Hyperspectral and LiDAR Features for Tree Species Mapping.” IEEE International Symposium on Geoscience and Remote Sensing IGARSS. New York, NY, USA: IEEE, 2017. 2879–2882. Print.
@inproceedings{8523651,
  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.},
  author       = {Liao, Wenzhi and Vancoillie, Frieke and Li, Liwei and Zhao, Bin and Gao, Lianru and Philips, Wilfried and Zhang, Bing},
  booktitle    = {IEEE International Symposium on Geoscience and Remote Sensing IGARSS},
  isbn         = {9781509049516},
  issn         = {2153-6996},
  keyword      = {Data fusion,remote sensing,hyperspectral image,LiDAR data,graph-based,CLASSIFICATION},
  language     = {eng},
  location     = {Fort Worth, TX, USA},
  pages        = {2879--2882},
  publisher    = {IEEE},
  title        = {Fusion of multi-scale hyperspectral and LiDAR features for tree species mapping},
  year         = {2017},
}

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