PIXEL-WISE AGRICULTURAL IMAGE TIME SERIES CLASSIFICATION: COMPARISONS AND A DEFORMABLE PROTOTYPE-BASED APPROACH

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. In this paper, we present and compare datasets and methods for both supervised and unsupervised pixel-wise segmentation of agricultural satellite image time series (SITS), and introduce a prototype-based method. Our approach learns deformable prototypes that can be transformed through time warping and spectral adjustments to reconstruct input sequences. The method can be trained with or without supervision and provides interpretable results through visualization of prototypes and their transformations. We demonstrate competitive performance on multiple agricultural SITS datasets while offering insights into crop temporal patterns. Our complete code is available at https://github.com/ElliotVincent/AgriITSC.