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

Volume

2

fYear

2008

fDate

7-11 July 2008

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;

fLanguage

English

Publisher

ieee

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

Type

conf

DOI

10.1109/IGARSS.2008.4778987

Filename

4778987