# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results
### Are Local and Global Temporal Pooling Layers Important? In this Section, we explore the use of pooling layers for different reach values. Pooling layers are generally used in image classification task to both speed-up the computations and make the learnt features more robust to noise [74]. They can be seen as a de-zooming operation, which naturally induces a multi-scale analysis when interleaved between successive convolutional layers. For a time \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RFs**} & \multicolumn{3}{c}{**RNNs**} & \multicolumn{3}{c}{**TempCNNs**} \\ \cline{2-10}  & **UA** & **PA** & **F-Score** & **UA** & **PA** & **F-Score** & **UA** & **PA** & **F-Score** \\ \hline
**Whe** & 94.53 & 94.86 & 94.70 & 95.03 & 94.75 & 94.89 & 95.21 & 95.97 & **95.59** \\
**Bar** & 90.08 & 58.49 & **70.92** & 67.28 & 61.52 & 64.27 & 82.59 & 59.90 & 69.44 \\
**Rap** & 96.52 & 92.70 & 94.57 & 94.86 & 91.26 & 93.02 & 98.34 & 95.61 & **96.96** \\
**Cor** & 91.74 & 97.43 & 94.50 & 95.24 & 96.02 & 95.63 & 94.04 & 97.55 & **95.76** \\
**Soy** & 91.82 & 71.52 & 80.41 & 83.36 & 78.52 & 80.87 & 84.83 & 82.86 & **83.83** \\
**Sun** & 90.09 & 92.15 & 91.11 & 90.02 & 93.27 & 91.62 & 91.02 & 94.56 & **92.76** \\
**Sor** & 75.02 & 30.66 & 43.53 & 66.14 & 49.66 & 56.73 & 78.02 & 47.13 & **58.77** \\
**Pea** & 98.91 & 44.42 & 61.30 & 72.20 & 59.22 & 65.07 & 85.66 & 69.93 & **77.00** \\
**Gra** & 91.54 & 87.01 & 89.22 & 92.49 & 93.69 & 93.09 & 92.84 & 94.92 & **93.87** \\
**Dec** & 81.09 & 68.18 & 74.08 & 78.79 & 75.74 & **77.24** & 79.57 & 74.18 & 76.78 \\
**Con** & 52.55 & 49.01 & 50.71 & 57.56 & 56.05 & 56.79 & 54.48 & 59.45 & **56.86** \\
**Wat** & 99.90 & 90.02 & 99.46 & 99.21 & 97.92 & 98.56 & 99.81 & 99.69 & **99.75** \\
**Urb** & 86.47 & 99.66 & 92.60 & 96.48 & 98.92 & 97.68 & 97.16 & 99.31 & **98.22** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Per-class quality measures. PA: producer’s accuracy. UA: user’s accuracy. Bold values represent the highest F-Score per class. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Reach** & **2** & **4** & **8** & **16** & **32** \\ \hline
**Whe** & 95.47 & 95.58 & **95.67** & 95.48 & 95.30 \\
**Bar** & 68.17 & 69.39 & **69.44** & 68.75 & 68.29 \\
**Rap** & 96.76 & **96.93** & 96.73 & 96.58 & 95.58 \\
**Cor** & 95.58 & 95.77 & **96.65** & 96.41 & 96.18 \\
**Soy** & 82.79 & 83.78 & **83.97** & 82.96 & 82.97 \\
**Sun** & 92.44 & 92.76 & 92.82 & 92.74 & **92.87** \\
**Sor** & 54.58 & 58.77 & **61.87** & 62.00 & 61.20 \\
**Pea** & 74.64 & **77.01** & 75.24 & 68.14 & 69.79 \\
**Gra** & 93.82 & 93.85 & **93.95** & 93.17 & 92.86 \\
**Dec** & 76.78 & 76.65 & 76.46 & 76.13 & **76.83** \\
**Con** & **56.57** & 56.49 & 54.21 & 53.88 & 53.80 \\
**Wat** & 99.76 & 99.76 & **99.78** & 99.66 & 99.70 \\
**Urb** & 98.16 & **98.22** & 98.15 & 97.58 & 97.43 \\ \hline
**OA** & 93.29 \(\pm\) 0.82 & 93.42 \(\pm\) 0.76 & **93.43 \(\pm\) 0.62** & 93.00 \(\pm\) 0.85 & 92.79 \(\pm\) 0.72 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Averaged Overall Accuracy (OA), \(\pm\)one standard deviation, and F-Score values for five reach values. Bold values highlights the highest values. series, these pooling layers simply reduce the length, and thus the resolution, of the time series that are output by the neurons--and this by a factor \(k\). Two types of pooling have received most of the attention in the literature in computer vision: 1) the local max-pooling ([PERSON], 1999), and 2) the global average pooling ([PERSON], 1999). For time series, global average pooling seems to have been more successful ([PERSON] et al., 2016; [PERSON] et al., 2017). We want to see here if these previous results can be generalized for time series classification. For this purpose, we train models with a global average pooling layer added after the third convolution layers for the following reach: 2, 4, 8, 16, and 32 days. We also train models with local pooling layers interleaved between each convolution layer with a window size \(k\) of 2.