Analysis of Multitemporal Classification Techniques for Forecasting Image Time Series

The classification of an annual time series by using data from past years is investigated in this letter. Several classification schemes based on data fusion, sparse learning, and semisupervised learning are proposed to address the problem. Numerical experiments are performed on a Moderate Resolutio...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2015-05, Vol.12 (5), p.953-957
Main Authors: Flamary, R., Fauvel, M., Dalla Mura, M., Valero, S.
Format: Article
Language:English
Subjects:
Classification
Forecasting
MODIS
Remote sensing
satellite image time series
Satellites
Support vector machines
Time series analysis
Training
transfer learning
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Summary:The classification of an annual time series by using data from past years is investigated in this letter. Several classification schemes based on data fusion, sparse learning, and semisupervised learning are proposed to address the problem. Numerical experiments are performed on a Moderate Resolution Imaging Spectroradiometer image time series and show that while several approaches have statistically equivalent performances, a support vector machine with I 1 regularization leads to a better interpretation of the results due to their inherent sparsity in the temporal domain.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2014.2368988