# Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping
## 6 Discussion  
In this section, the experimental results presented in Section 5 are discussed in the same order, starting from the large-scale classification analysis of Section 5.1. In Table 4 all considered swaths  
Figure 6: Four analyzed patches of \(512\times 512\) pixels are placed along the rows. They are indicated as (**a–d**), selected from the classification results over the stack number 7 defined in Table 2. Each column addresses to a different quantity: (REF) is the modified FROM-GLC reference with the clearcuts marked by PRODES between 2017 and 2019 as invalids (INV), (S-1, ORIG) is the Random Forests classification map using the input parameters from [7] only, (S-1, SADH) is the Random Forests result adding the SADH textures to the original parameters, (S-2, RGB) and (S-2, NDVI) are the optical True Color and NDVI maps, respectively, of Sentinel-2 acquisitions from the considered month. (REF), (S-1, ORIG), and (S-1, SADH) maps follow the legend described in Figure 5, while (S-2, NDVI) map comprises three classes: _missing data_ (MDA) in black, _forests_ (FOR) in green, and _other structures_ (OTH) in white. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Class** & **Patch (a)** & **Patch (b)** & **Patch (c)** & **Patch (d)** \\ \hline \multirow{3}{*}{**pixels**} & _ART_ & 20419 & 7706 & 0 & 0 \\  & _FOR_ & 118922 & 86290 & 216741 & 141669 \\  & _NFR_ & 100506 & 155408 & 42073 & 117044 \\ \hline \multirow{2}{*}{**Case**} & **Metric** & **Patch (a)** & **Patch (b)** & **Patch (c)** & **Patch (d)** \\ \hline \multirow{2}{*}{_ORIG_} & _OA_ & 72.31\% & 80.71\% & 94.63\% & 85.88\% \\  & _AA_ & 75.94\% & 79.96\% & 93.04\% & 85.79\% \\ \hline \multirow{2}{*}{_SADH_} & _OA_ & 73.60\% & 82.49\% & 95.75\% & 87.98\% \\  & _AA_ & 78.15\% & 85.98\% & 94.28\% & 87.88\% \\ \hline \multirow{3}{*}{_SA_OA_} & \(\Delta_{\text{OA}}\) & 1.29\% & 1.78\% & 1.12\% & 2.10\% \\  & \(\Delta_{\text{AA}}\) & 2.21\% & 6.02\% & 1.24\% & 2.09\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Overall accuracy (OA) and average accuracy (AA) for the four patches in Figure 6: patch (**a**) and patch (**b**) are characterized by urban areas, while patch (**c**) and patch (**d**) contain clear-cuts. \(\Delta_{\text{OA}}\) and \(\Delta_{\text{AA}}\) represent the increment in (OA) and (AA), respectively, when including textures within the classification. are characterized by an overall accuracy above 82.50% and 84.26% for the (_ORIG_) and (_SADH_) cases, respectively, by considering all valid pixels within the images. Again, the additional information of the SADH textures increases both the OA and the AA of at least 1.5% in all four swaths. In particular, the use of the SADH textures allows the Random Forests to improve the detection performance, especially for the ART and NFR classes, depicted in blue and red, respectively, in Figure 4 and better visible with the analysis of the global confusion matrices in Figure 7.  
Figure 8 shows the pie charts related to the feature importance in the two considered cases ORIG (a) and SADH (b) by grouping all the texture features together. We notice that the set of texture features has quite a strong relevance in the Random Forests prediction and improves the classification accuracy presented in Table 4. Furthermore, in Figure 8c we show how the features importance is distributed for the 18 SADH textures only, partitioning the green slice of the pie chart in Figure 8b only. Here we can see how the entropy (_ENT_) plays a key role among all other features, while the less informative is the pure variance (_VAR_). Figure 8: Feature importance for the two analyzed cases, (**a**) ORIG and (**b**) SADH; in particular, pie chart (**c**) shows the distribution of importance for the SADH textures only. Figure 7: Confusion matrices for the whole test dataset, considering the two analyzed cases, (ORIG) and (SADH). The sum of all the elements along each column correspond to the total number of pixels associated to each class.