# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results
### Are Local and Global Temporal Pooling Layers Important?
As local pooling layers virtually increase the reach of the following convolutional layers for this experiment, we kept the reach constant--2, 4, 8, 16, and 32 days--by reducing the convolution filter size \(f\) after each convolution. For example, a constant reach of 8 is obtained by applying successively three convolutions with filter sizes of 9, 5, and 3, with a local pooling layer after each convolutional one. Figure 9 displays the OA values as a function of reach. Each curve represents a different configuration: local max-pooling (MP) in blue, local max-pooling and global average pooling (MP + GAP) in orange, local average pooling (AP) in yellow, local and global average pooling (AP + GAP) in purple, and global average pooling (GAP) in green. The horizontal red dashed line corresponds to the OA values obtained without pooling layers in the previous experiment. Figure 9 shows that the use of pooling layers performs poorly: the OA results are almost always below the one obtained without pooling layers (red dashed line). Let us describe in more details the different findings for both global and local pooling layers. The use of a global average pooling layer leads to the biggest decrease in accuracy. This layer is generally used to drastically reduce the number of trainable parameters by reducing the size of the last convolution layer to its depth. It thus performs an extreme dimensionality reduction, that decreases here the accuracy performance. Regarding the use of only local pooling layers, Figure 9 shows similar results for both max and average pooling layers. The OA values tend to decrease when the reach increases. The results are similar to those obtained by the model without pooling layers (horizontal red dashed line) for reach values lower than nine days, with even a slight improvement when using a local average pooling layer with a constant reach of four days. Figure 9: Overall Accuracy (OA) as function of reach for local max-pooling (MP) in blue, local max-pooling and global average pooling (MP + GAP) in orange, local average pooling (AP) in yellow, local and global average pooling (AP + GAP) in purple, and global average pooling (GAP) alone in green. We use the dataset with three spectral bands and a regular temporal sampling at two days. This last result contrasts with results obtained in computer vision tasks for which: (1) max-pooling tends to give better results than average pooling, and (2) the use of local pooling layers helps to improve the classification performance. The main reason for this difference is probably task-related. In image classification, local pooling layers are known to extract features that are invariant to the scale and small transformations leading to models that can detect objects in an image no matter their locations or their sizes. However, the location of the temporal features, and their amplitude, are critical for SITS classification. For example, winter and summer crops, that we want to distinguish, may have similar profiles with only a shift in time. Removing the temporal location of the peak of greenness might prevent their discrimination.