Abstract
This study deals with data fusion of hyperspectral and LiDAR sensors for tree species mapping in complex, closed forest canopies in Belgium. In particular, seven tree species were mapped: Beech, Ash, Larch, Poplar, Copper beech, Chestnut and Oak. The added value of LiDAR height profile data on tree species mapping was assessed. Sensor data were fused in the PCA domain, while optimal feature combination was derived from the best classification performance (in terms of Kappa and producer's accuracy) based on 5-fold cross-validation. Besides, varying training set sizes were tested (resp. 10%, 30% and 50% number of samples per tree species class). Feature fusion of PCA-transformed HS and LiDAR data was most effective for small sample set sizes reaching a Kappa accuracy improvement of 10.51%.
Original language | English |
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Pages | 1-4 |
Number of pages | 4 |
DOIs | |
Publication status | Published - 2015 |
Event | 7th workshop on hyperspectral image and signal processing : evolution in remote sensing 2015 - Tokyo, Japan Duration: 2 Jun 2015 → 5 Jun 2015 |
Workshop
Workshop | 7th workshop on hyperspectral image and signal processing : evolution in remote sensing 2015 |
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Country/Territory | Japan |
City | Tokyo |
Period | 2/06/15 → 5/06/15 |
Keywords
- LiDAR
- hyperspectral
- feature fusion
- closed forest canopy
- tree species mapping
- atmospheric modeling
- geophysical image processing
- image classification
- principal component analysis