Time-Space Tradeoff in Deep Learning Models for Crop Classification on Satellite Multi-Spectral Image Time Series

Vivien Sainte Fare Garnot , Loic Landrieu , Sébastien Giordano (1) , N. Chehata (2)

1 MATIS - Méthodes d'Analyses pour le Traitement d'Images et la Stéréorestitution
2 Bordeaux INP - Institut Polytechnique de Bordeaux

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.

Mots clés: Index Terms-Deep learning; Crop type classification; Multi-temporal; Sentinel-2

Domaines: Apprentissage [cs.LG]; Réseau de neurones [cs.NE]; Traitement des images [eess.IV]; Intelligence artificielle [cs.AI]

Vivien Sainte Fare Garnot, Loic Landrieu, Sébastien Giordano, N. Chehata. Time-Space Tradeoff in Deep Learning Models for Crop Classification on Satellite Multi-Spectral Image Time Series. IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium, Jul 2019, Yokohama, Japan. pp.6247-6250, ⟨10.1109/IGARSS.2019.8900517⟩. ⟨hal-02386701⟩