  Although recently several foundation models for satellite remote sensing
imagery have been proposed, they fail to address major challenges of
real/operational applications. Indeed, embeddings that don't take into account
the spectral, spatial and temporal dimensions of the data as well as the
irregular or unaligned temporal sampling are of little use for most real world
uses.As a consequence, we propose an ALIgned Sits Encoder (ALISE), a novel
approach that leverages the spatial, spectral, and temporal dimensions of
irregular and unaligned SITS while producing aligned latent representations.
Unlike SSL models currently available for SITS, ALISE incorporates a flexible
query mechanism to project the SITS into a common and learned temporal
projection space. Additionally, thanks to a multi-view framework, we explore
integration of instance discrimination along a masked autoencoding task to
SITS. The quality of the produced representation is assessed through three
downstream tasks: crop segmentation (PASTIS), land cover segmentation
(MultiSenGE), and a novel crop change detection dataset. Furthermore, the
change detection task is performed without supervision. The results suggest
that the use of aligned representations is more effective than previous SSL
methods for linear probing segmentation tasks.
