Time-Space tradeoff in deep learning models for crop classification on satellite multi-spectral image time series
This work provides guidelines for designing deep learning models in agricultural remote sensing, but it is incremental as it builds on existing methods.
The study investigated structured deep learning models for crop type classification using satellite multi-spectral image time series, finding that hybrid configurations allocating up to 90% of parameters to temporal structure performed best.
In this article, we investigate several structured deep learning models for crop type classification on multi-spectral time series. In particular, our aim is to assess the respective importance of spatial and temporal structures in such data. With this objective, we consider several designs of convolutional, recurrent, and hybrid neural networks, and assess their performance on a large dataset of freely available Sentinel-2 imagery. We find that the best-performing approaches are hybrid configurations for which most of the parameters (up to 90%) are allocated to modeling the temporal structure of the data. Our results thus constitute a set of guidelines for the design of bespoke deep learning models for crop type classification.