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

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, the multivariate nature of the time-series and the scalability to large data-sets. 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.