Pixel and region based temporal classification fusion for HR Satellite Image Time Series

Author

Réjichi, S. ; Châabane, F.

Author_Institution

COSIM Lab., Carthage Univ., Tunis, Tunisia

fYear

2012

fDate

22-27 July 2012

Firstpage

435

Lastpage

438

Abstract

Satellite Image Time Series (SITS) are a very useful source of information for geoscientists especially for land cover monitoring. In this paper a new temporal classification approach for High Resolution (HR) SITS is proposed. It suggests a Bayesian combination between a pixel and a region, SVM (Support Vector Machine) based techniques where SVM is considered as a probabilistic classifier. RBF (Radial Basis Function) based SVM kernel is used to classify pixel evolution while a graph based SVM kernel is considered to analyze region temporal behavior.

Keywords

Bayes methods; artificial satellites; geophysical image processing; graph theory; image classification; image fusion; image resolution; radial basis function networks; support vector machines; terrain mapping; time series; Bayesian combination; HR-SITS; RBF; graph-based SVM kernel; high resolution SITS; land cover monitoring; pixel-based temporal classification fusion; probabilistic classifier; radial basis function; region-based temporal classification fusion; satellite image time series; support vector machine; Bayesian methods; Kernel; Probabilistic logic; Satellites; Support vector machines; Time series analysis; Vegetation mapping; Bayesian fusion; Graph kernel; HR-SITS; SVM classifier; Temporal classification;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International

Conference_Location

Munich

ISSN

2153-6996

Print_ISBN

978-1-4673-1160-1

Electronic_ISBN

2153-6996

Type

conf

DOI

10.1109/IGARSS.2012.6351545

Filename

6351545