Leveraging Covariance Representations With Riemannian Resnet for Satellite Image Time Series Classification

Remote sensing has become an essential tool for monitoring agricultural practices, enabling the assessment of crop health, phenology and yield on unprecedented scales. In particular, satellite image time series (SITS) acquired by constellations such as Sentinel capture rich spatial, spectral and temporal patterns essential for identifying crop types. However, the analysis of SITS data is difficult due to their large volume, complexity, and variability. Robust representations, such as covariance matrices, offer a promising solution by encoding second-order statistics that are both compact and resistant to noise. Building upon the success of covariance matrices in brain signal classification task, we propose an approach that leverages the structure of covariance matrices for SITS classification, with specialized tools from Riemannian geometry for efficient processing of the covariance matrices. Specifically, the three main contributions are (i) estimating spectral and temporal covariance matrices from SITS, (ii) evaluating the Riemannian residual network (RResNet) for processing single representations, and (iii) introducing the LogEucResNet-Combo (LERN-Combo) model to leverage both spectral and temporal covariance matrices. Experiments conducted on the BreizhCrops dataset demonstrate that LERN-Comboh achieves competitive results, offering a robust and parameter-efficient alternative for SITS analysis.