  Using images acquired by different satellite sensors has shown to improve
classification performance in the framework of crop mapping from satellite
image time series (SITS). Existing state-of-the-art architectures use
self-attention mechanisms to process the temporal dimension and convolutions
for the spatial dimension of SITS. Motivated by the success of purely
attention-based architectures in crop mapping from single-modal SITS, we
introduce several multi-modal multi-temporal transformer-based architectures.
Specifically, we investigate the effectiveness of Early Fusion, Cross Attention
Fusion and Synchronized Class Token Fusion within the Temporo-Spatial Vision
Transformer (TSViT). Experimental results demonstrate significant improvements
over state-of-the-art architectures with both convolutional and self-attention
components.
