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Journal Articles Statistics and Computing Year : 2022

Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series

Abstract

The classification of irregularly sampled Satellite image time-series (SITS) is investigated in this paper. A multivariate Gaussian process mixture model is proposed to address the irregular sampling and the multivariate nature of the time-series. The spectral and temporal correlation is handled using a Kronecker structure on the covariance operator of the Gaussian process. The multivariate Gaussian process mixture model allows both for the classification of time-series and the imputation of missing values. Experimental results on simulated and real SITS data illustrate the importance of taking into account the spectral correlation to ensure a good behavior in terms of classification accuracy and reconstruction errors.
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Dates and versions

hal-03280484 , version 1 (07-07-2021)
hal-03280484 , version 2 (02-09-2022)

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Alexandre Constantin, Mathieu Fauvel, Stéphane Girard. Mixture of multivariate Gaussian processes for classification of irregularly sampled satellite image time-series. Statistics and Computing, 2022, 32 (5), pp.79. ⟨10.1007/s11222-022-10145-8⟩. ⟨hal-03280484v2⟩
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