Title :
Vegetation Species Identification Using Hyperspectral Imagery
Author :
Hu, Baoxin ; Lévesque, Josée ; Ardouin, Jean-Pierre
Author_Institution :
Dept. of Earth & Space Sci. & Eng., York Univ., Toronto, ON
Abstract :
A good similarity measure is very important to ensure accurate classification of vegetation species using hyperspectral remote sensing data. In this study, the effectiveness of the existing similarity measures, spectral angle mapper (SAM), Euclidean distance (ED), and spectral information divergence (SID) was evaluated using data measured by a field spectrometer. To overcome the limitations of the existing measures, a new metric was developed based on the concept of conditional entropy.
Keywords :
entropy; remote sensing; spectrometers; vegetation; ED; Euclidean distance; SAM; SID; conditional entropy; hyperspectral imagery; remote sensing data; spectral angle mapper; spectral information divergence; spectrometer; vegetation species classification; vegetation species identification; Entropy; Euclidean distance; Extraterrestrial measurements; Geoscience; Hyperspectral imaging; Hyperspectral sensors; Reflectivity; Remote monitoring; Remote sensing; Vegetation mapping; Conditional entropy; Similarity measure; Vegetation species classification;
Conference_Titel :
Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International
Conference_Location :
Boston, MA
Print_ISBN :
978-1-4244-2807-6
Electronic_ISBN :
978-1-4244-2808-3
DOI :
10.1109/IGARSS.2008.4778987
