  Improvements in Earth observation by satellites allow for imagery of ever
higher temporal and spatial resolution. Leveraging this data for agricultural
monitoring is key for addressing environmental and economic challenges. Current
methods for crop segmentation using temporal data either rely on annotated data
or are heavily engineered to compensate the lack of supervision. In this paper,
we present and compare datasets and methods for both supervised and
unsupervised pixel-wise segmentation of satellite image time series (SITS). We
also introduce an approach to add invariance to spectral deformations and
temporal shifts to classical prototype-based methods such as K-means and
Nearest Centroid Classifier (NCC). We study different levels of supervision and
show this simple and highly interpretable method achieves the best performance
in the low data regime and significantly improves the state of the art for
unsupervised classification of agricultural time series on four recent SITS
datasets.
