# Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series
## 4 Experimental Results
### Benefiting from Both Spectral and Temporal Dimensions  
In this experiment, we compare four configurations: (1) no guidance, (2) only temporal guidance, (3) only spectral guidance, and (4) both temporal and spectral guidance (TempCNNs). Before presenting the obtained results, we first describe the trained models for these fourth types of guidance, which are illustrated in Figure 8.  
**No guidance**: Similar to a traditional classifier, such as the RF algorithm, the first considered type of model ignores the spectral and temporal structures of the data, i.e., the expected model is the same regardless of the order in which the features (spectral and temporal) are given. For this configuration, we decided to train two types of algorithms: (1) the RF classifier selected as the competitor [3], and (2) a deep learning model composed of three dense layers of 1024 units--this specific architecture is named FC in the following. Both models are trained the whole dataset with \(T\times D\) features, with \(T\) the length of the times series and \(D\) the number of spectral features (see Table 2). As both RF and FC models do not require regular temporal sampling, the use of 2-day sampling is not necessary and can even lead to under-performance. The use of high dimensional space composed of redundant and sometimes noisy features may indeed decrease accuracy. Hence, the results of both models are displayed for the original temporal sampling, i.e., \(T=46\). **Temporal guidance**: The second type of model provides guidance only along the temporal dimension. We train an architecture with convolution filters of size \((f\times 1)\). In other words, the same convolution filters are applied across the temporal dimension, identically for all the spectral dimensions (second column in Figure 8). **Spectral guidance:** The third type of model includes guidance only on the spectral dimension (third column in Figure 8). For this purpose, a convolution of size \((1,D)\) is first applied without padding, reducing the spectral dimension to one for the next convolution layers of size \((1\times 1)\). **Spectro-temporal guidance:** The last type of model corresponds to the one presented in Section 2.3 (TempCNNs), where the first convolutions have size \((f\times D)\) (last column in Figure 8). The choice of this architecture is explained in the following sections. We also compare this architecture to RNNs, which also provide a spectro-temporal guidance (Section 3.4). It is interesting to note that studying temporal and spectral guidance separately is not standard; we only include it here as a means to disentangle the contribution of the different components of our TempCNN model. As illustrated by Figure 8, all the applied convolutions are 1D-convolutions as they slide over one dimension only: over time for both TempCNN and the temporal guidance, and the spectral dimension for the spectral guidance. Table 3 displays the Overall Accuracy (OA) and one standard deviation for the four levels of guidance. As the use of engineering features may help the different models, we train all the models for the three types of features presented in Section 3.3: NDVI alone, spectral bands (SB), and spectral  
Figure 8: Types of guidance. The top row represents our input in a 2D array, whereas the bottom row represents the output of one neuron under each guidance scheme. bands with three spectral indices (SB-SF). For both models using temporal guidance, the filter size \(f\) is set to five. All the models are learned as specified in Section 2.3, including dropout and batch normalization layers, weight decay and the use of a validation set. Table 3 shows that the OA increases for CNN models when adding more guidance, regardless of the type of used features. Note that the case of only using the spectral guidance with NDVI feature is a particular \"degenerate\" case: the spectral dimension is composed of only one feature (NDVI). The trained model applies convolutions of size (1,1), leading to a model that does not provide any guidance.