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 language | English |
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Pages | 2879-2882 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 4 Dec 2017 |
Event | (2017) IEEE International Symposium on Geoscience and Remote Sensing IGARSS. - Fort Worth, United States Duration: 23 Jul 2017 → 28 Jul 2017 |
Conference
Conference | (2017) IEEE International Symposium on Geoscience and Remote Sensing IGARSS. |
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Abbreviated title | IGARSS 2017 |
Country/Territory | United States |
City | Fort Worth |
Period | 23/07/17 → 28/07/17 |
Keywords
- data fusion
- remote sensing
- hyperspectral image
- LiDAR data
- graph-based
- classification
- geophysical image processing
- image classification