## 5 Experimental Results and Discussion
### Classification
In [76], features from optical and SAR were extracted and used by the committee of neural networks of multilayer perceptrons to classify a diverse agriculture region considerably. Recurrent encoders have been employed in [79] to classify a large area for 17 considered classes using high spatial resolution (10 m) sentinel-2 data and achieved 90% OAA, which proved that recurrent encoders are useful to capture the temporal information of spectral features that leads to higher accuracy. [PERSON] et al. [77] also used sentinel-2 data and achieved a maximum 95% classification accuracy using RF classifier but nine classes were considered. In conclusion, it is interesting to notice neuron activation inside the network during the classification process. Indeed, it is possible to plot unit values when the network receives specific inputs and compare how model behaviors change. In Figure 12 four samples, belonging to the same class \"artificials\", feed the model creating individual activations in the input layer. Even if they all belong to the same class, the four instances took into account present a noticeable variance. Either the spectral features in a specific time instance (rows) or their temporal variation (columns) present different patterns that make them difficult to classify. However, already after the first LSTM layer with the TimeDistributedDense block, the resulting \(9\times 9\) matrices \(Y_{(timeD)}\) have a clear pattern that can be used by the following layers to classify the different instances in their respective classes. So, the network during the training process learns to identify specific temporal schemes, that allows making strong distributed and disentangle representations. Figure 11: (**a**) Final classified map using Pixel R-CNN, (**b**) zoomed in region of the classified map, and (**c**) Raw Sentinel-2 RGB composite of the zoomed region. \begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Ground Truth**} & \multicolumn{10}{c}{**Classified Classes**} & \multicolumn{10}{c}{**Total**} & \multicolumn{1}{c}{**PA**} \\ \cline{2-13}  & **TM** & **AR** & **TR** & **RY** & **WH** & **SY** & **AP** & **PR** & **GL** & **WT** & **LN** & **DW** & **YY** & **BL** & **MZ** & & & \\ \hline Tomatoes (TM) & **1096** & 0 & 0 & 0 & 0 & 4 & 11 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1111 & 98\% \\ Artificial (AR) & 0 & **3752** & 8 & 1 & 2 & 0 & 2 & 1 & 9 & 9 & 12 & 2 & 6 & 0 & 4 & 3808 & 99\% \\ Trees (TR) & 0 & 31 & **2967** & 1 & 0 & 0 & 0 & 3 & 10 & 0 & 17 & 0 & 2 & 0 & 0 & 3031 & 98\% \\ Weg (RY) & 0 & 1 & 0 & **1960** & 25 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 5 & 0 & 1991 & 98\% \\ Wheat (WH) & 38 & 7 & 0 & 221 & **4981** & 6 & 0 & 0 & 10 & 0 & 14 & 1 & 2 & 38 & 42 & 5360 & 93\% \\ Soya (SY) & 3 & 0 & 0 & 0 & 3 & **1226** & 0 & 0 & 0 & 0 & 10 & 0 & 3 & 0 & 41 & 1287 & 95\% \\ Apple (AP) & 0 & 0 & 0 & 0 & 0 & **142** & 0 & 0 & 0 & 2 & 0 & 21 & 0 & 0 & 165 & 86\% \\ Peer (PR) & 0 & 0 & 11 & 0 & 0 & 0 & 27 & **124** & 0 & 0 & 0 & 0 & 6 & 0 & 0 & 168 & 73\% \\ Grassland (GL) & 0 & 39 & 3 & 7 & 0 & 1 & 0 & 0 & **239** & 0 & 72 & 0 & 3 & 0 & 4 & 368 & 65\% \\ Water (WT) & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & **906** & 0 & 0 & 0 & 0 & 0 & 0 & 906 & 100\% \\ Lucerne (LN) & 0 & 0 & 0 & 2 & 0 & 2 & 0 & 48 & 0 & **7250** & 0 & 26 & 0 & 10 & 7338 & 98\% \\ Durum.Wheat (W) & 0 & 4 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 0 & 0 & **322** & 0 & 0 & 0 & 328 & 98\% \\ Vineyard (VV) & 11 & 7 & 4 & 4 & 11 & 1 & 50 & 1 & 21 & 0 & 93 & 0 & **1239** & 0 & 7 & 2349 & 91\% \\ Barley (BL) & 0 & 1 & 0 & 2 & 24 & 0 & 0 & 1 & 0 & 1 & 0 & 0 & **817** & 0 & 846 & 96\% \\ Maize (MZ) & 17 & 14 & 0 & 10 & 24 & 0 & 3 & 10 & 0 & 16 & 1 & 6 & 0 & **7689** & 7790 & 99\% \\ Total & 1165 & 3856 & 2993 & 2198 & 5060 & 1271 & 221 & 132 & 350 & 915 & 7488 & 326 & 2214 & 860 & 7797 & 99\% \\ UA & 94\% & 97\% & 99\% & 89\% & 98\% & 96\% & 64\% & 93\% & 68\% & 99\% & 96\% & 99\% & 96\% & 95\% & 98\% & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Obtained confusion matrix.