# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 5 Conclusions  
This work explored the use of TempCNNs for SITS classification. Through an extensive set of experiments carried out on a series of 46 Formosat-2 images, we showed that the proposed TempCNN model outperforms RF and RNN algorithms by a margin of 1 to 3% in overall accuracy. A visual analysis also shows the good quality of TempCNN to accurately map land cover without over representation of majority classes.  
To provide intuitions beyond this good performance, we studied the impact of the network architecture by varying the depth and the width of the models, by looking at the influence of common regularization mechanisms, and by exploring different batch size values. We have also demonstrated the importance of using both temporal and spectral dimensions when computing the convolutions. The remaining experimental results support two main recommendations on the use of pooling layers and the manual engineering of spectral features. First, we show that the use of global pooling layers, which drastically reduces the number of trainable parameters, is armful for SITS classification. Overall, we recommend careful study of the influence of pooling layers before any integration into a TempCNN network, and to favour local average pooling. Second, we show that the addition of manually-calculated spectral features, such as the NDVI, does not seem to improve TempCNN models. We thus recommend not to use them.  
All these results show that TempCNNs are a very strong learner for Sentinel-2 SITS, which presents a high spectral resolution with 10 bands at a spatial resolution of 10 and 20 m. The presence of salt and pepper noise also indicates a need to take the spatial dimension of SITS into account, in addition to the spectral and temporal dimensions. Integrating the spatial dimension represents a good avenue for future research.  
All the authors have been involved in the writing of the manuscript and the design of experiments. C.P. has run the different experiments. Investigation, C.P.; Methodology, C.P., G.I.W. and F.P.; Writing--review & editing, C.P., G.I.W. and F.P.  
[PERSON] is the recipient of an Australian Research Council Discovery Early Career Award (project number DE170100037) funded by the Australian Government. This material is based upon work supported by the Air Force Office of Scientific Research, Asian Office of Aerospace Research and Development (AOARD) under award number FA2386-18-1-4030.  
Figure 11: Visual results for two areas. The first column displays the Formosat-2 image in false color from 14 July 2006. The second and third columns give the results for Random Forest (RF) and our TempCNN model, respectively. The images in the last column displays in red the disagreement between both classifiers. Legend of land cover maps is given in Table 1.  
AcknowledgmentsThe authors would like to thank their colleagues from the [PERSON] laboratory ([PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]) for providing the reference land-cover maps as well as the corrected Formosat-2 images. They would also like to thank [PERSON] and [PERSON] for their comments on the manuscript, as well as anonymous reviewers for their very constructive comments.