  The unprecedented availability of spatial and temporal high-resolution
satellite image time series (SITS) for crop type mapping is believed to
necessitate deep learning architectures to accommodate challenges arising from
both dimensions. Recent state-of-the-art deep learning models have shown
promising results by stacking spatial and temporal encoders. However, we
present a Pyramid Time-Series Transformer (PTST) that operates solely on the
temporal dimension, i.e., neglecting the spatial dimension, can produce
superior results with a drastic reduction in GPU memory consumption and easy
extensibility. Furthermore, we augment it to perform semi-supervised learning
by proposing a classification-friendly VAE framework that introduces clustering
mechanisms into latent space and can promote linear separability therein.
Consequently, a few principal axes of the latent space can explain the majority
of variance in raw data. Meanwhile, the VAE framework with proposed tweaks can
maintain competitive classification performance as its purely discriminative
counterpart when only $40\%$ of labelled data is used. We hope the proposed
framework can serve as a baseline for crop classification with SITS for its
modularity and simplicity.
