Multiapproach System Based on Fusion of Multispectral Images for Land-Cover Classification

Satellite image classification is usually marked by several types of imperfection such as uncertainty, imprecision, and ignorance. Data fusion of additional sensors tries to overcome the types of imperfection by using probability, possibility, and evidence theories. Our approach will lead to improve...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2008-12, Vol.46 (12), p.4153-4161
Main Authors: Farah, I.R., Boulila, W., Ettabaa, K.S., Ben Ahmed, M.
Format: Article
Language:English
Subjects:
Applied geophysics
Classification
Computer Science
data fusion
data imperfection
Defects
Earth sciences
Earth, ocean, space
Environmental Sciences
evidence theory
Exact sciences and technology
Image classification
Internal geophysics
Land cover
Laser radar
Mathematics
Multiagent systems
Multispectral imaging
Optical sensors
Optimization
possibility theory
probability theory
Radar imaging
Radar tracking
Remote sensing
Satellites
Sensor fusion
Tracking
Uncertainty
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Summary:Satellite image classification is usually marked by several types of imperfection such as uncertainty, imprecision, and ignorance. Data fusion of additional sensors tries to overcome the types of imperfection by using probability, possibility, and evidence theories. Our approach will lead to improve classification accuracy of satellite images by choosing the optimum theory for a particular image context and proposing a theoretical framework based on a multiagent system and case-based reasoning. We validate our approach trough a set of optical images from the satellite Satellite Positioning and Tracking 4 and radar images from the European Remote Sensing satellite 2, and we show that the overall accuracy is considerably increased from 83% for maximum-likelihood classification applied to multispectral imagery to 94% with the proposed approach.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2008.2001554