Hyperspectral discrimination of tropical dry forest lianas and trees: Comparative data reduction approaches at the leaf and canopy levels
A dataset of spectral signatures (leaf level) of tropical dry forest trees and lianas and an airborne hyperspectral image (crown level) are used to test three hyperspectral data reduction techniques (principal component analysis, forward feature selection and wavelet energy feature vectors) along with pattern recognition classifiers to discriminate between the spectral signatures of lianas and trees. It was found at the leaf level the forward waveband selection method had the best results...[Show more]
|Collections||ANU Research Publications|
|Source:||Remote Sensing of Environment|
|01_Kalacska_Hyperspectral_discrimination_2007.pdf||1.23 MB||Adobe PDF||Request a copy|
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