XAI for Early Crop Classification
Ayshah Chan Maja Schneider Marco Körner
We propose an approach for early crop classification through identifying
important timesteps with eXplainable AI (XAI) methods. Our approach consists of
training a baseline crop classification model to carry out layer-wise relevance
propagation (LRP) so that the salient time step can be identified. We chose a
selected number of such important time indices to create the bounding region of
the shortest possible classification timeframe. We identified the period 21st
April 2019 to 9th August 2019 as having the best trade-off in terms of accuracy
and earliness. This timeframe only suffers a 0.75% loss in accuracy as compared
to using the full timeseries. We observed that the LRP-derived important
timesteps also highlight small details in input values that differentiates
between different classes and