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Large-scale crop type classification is a task at the core of remote sensingefforts with applications of both economic and ecological importance. Currentstate-of-the-art deep learning methods are based on self-attention and usesatellite image time series (SITS) to discriminate crop types based on theirunique growth patterns. However, existing methods generalize poorly to regionsnot seen during training mainly due to not being robust to temporal shifts ofthe growing season caused by variations in climate. To this end, we proposeThermal Positional Encoding (TPE) for attention-based crop classifiers. Unlikeprevious positional encoding based on calendar time (e.g. day-of-year), TPE isbased on thermal time, which is obtained by accumulating daily averagetemperatures over the growing season. Since crop growth is directly related tothermal time, but not calendar time, TPE addresses the temporal shifts betweendifferent regions to improve generalization. We propose multiple TPEstrategies, including learnable methods, to further improve results compared tothe common fixed positional encodings. We demonstrate our approach on a cropclassification task across four different European regions, where we obtainstate-of-the-art generalization results.

Generalized Classification of Satellite Image Time Series with Thermal Positional Encoding