Sub-pixel land-cover classification with SPOT-VEGETATION imagery

Author

Swinnen, Else ; Eerens, Herman ; Lissens, Gil ; Canters, Frank

Author_Institution

Centre of Expertise on Remote Sensing & Atmos. Processes, Flemish Inst. for Technol. Res., Mol, Belgium

Volume

1

fYear

2001

fDate

2001

Firstpage

537

Abstract

Knowledge about global land cover is an important input for the modelling of ecological and environmental processes. Production of such global vegetation maps can be facilitated by using automated methods for classification. Two neural network strategies, an overall and class-specific network(s), were tested on a part of Europe. This study indicates that sub-pixel proportion estimates can be assessed quite accurately from 1-km resolution SPOT-VEGETATION imagery

Keywords

geophysical signal processing; image classification; neural nets; vegetation mapping; Europe; SPOT-VEGETATION imagery; automated methods; class-specific neural network; classification; global land cover; global vegetation maps; neural network strategies; overall neural network; sub-pixel land-cover classification; Biological system modeling; Continents; Gas insulated transmission lines; Image resolution; Neural networks; Production; Remote sensing; Spatial databases; Spatial resolution; Vegetation mapping;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International

Conference_Location

Sydney, NSW

Print_ISBN

0-7803-7031-7

Type

conf

DOI

10.1109/IGARSS.2001.976214

Filename

976214