  New remote sensing sensors now acquire high spatial and spectral Satellite
Image Time Series (SITS) of the world. These series of images are a key
component of classification systems that aim at obtaining up-to-date and
accurate land cover maps of the Earth's surfaces. More specifically, the
combination of the temporal, spectral and spatial resolutions of new SITS makes
possible to monitor vegetation dynamics. Although traditional classification
algorithms, such as Random Forest (RF), have been successfully applied for SITS
classification, these algorithms do not make the most of the temporal domain.
Conversely, some approaches that take into account the temporal dimension have
recently been tested, especially Recurrent Neural Networks (RNNs). This paper
proposes an exhaustive study of another deep learning approaches, namely
Temporal Convolutional Neural Networks (TempCNNs) where convolutions are
applied in the temporal dimension. The goal is to quantitatively and
qualitatively evaluate the contribution of TempCNNs for SITS classification.
This paper proposes a set of experiments performed on one million time series
extracted from 46 Formosat-2 images. The experimental results show that
TempCNNs are more accurate than RF and RNNs, that are the current state of the
art for SITS classification. We also highlight some differences with results
obtained in computer vision, e.g. about pooling layers. Moreover, we provide
some general guidelines on the network architecture, common regularization
mechanisms, and hyper-parameter values such as batch size. Finally, we assess
the visual quality of the land cover maps produced by TempCNNs.
