Analysis of multi- temporal classification techniques for forecasting image time series

Description

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 l(1) regularization leads to a better interpretation of the results due to their inherent sparsity in the temporal domain.

Abstract

International audience

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