# Multi-Temporal Sentinel-1 Backscatter and Coherence for Rainforest Mapping
## 6 Discussion
The elements along the diagonal of the confusion matrix correspond to the correctly predicted pixels, class-by-class. The color associated to each element corresponds to the percentage obtained by normalizing the current element for the total number of samples for each specific class. Moreover, during the investigation, we noted that an additional source of inconsistency between the reference and the resulting classification map is given by the different semantic interpretation of radar and optical sensors over some areas characterized by the presence of rough terrain and sparse vegetation. As an example, we analyze the white patch in Figure 4, of size 1024 \(\times\) 1024 pixels, corresponding to a portion of stack 8 in Table 2. It corresponds to an area of the Pacaas Novos National Park. As reported in the corresponding S-2 RGB map of Figure 9 that area comprises a plateau with a peak in the upper-right side of the patch, called mount Tracoa. Through a visual comparison between the NDVI and RGB maps, retrieved from the S-2 acquisition of 2019, and the REF reference of 2017, it is now clear that the latter is not really sensitive to the presence of low and sparse vegetation. On the other hand, the result obtained from case (_SADH_) clearly show the same patterns visible within the optical data. The reader should therefore be aware that these kinds of discrepancies, even if they actually represent a source of information, might slightly decrease the computed performance of the proposed methodology, calculated by considering the REF only as external reference map. By observing the REF reference and the results of case (ORIG) and case (SADH) in Figure 6, it can be seen how the introduction of the texture information in case (_SADH_) helps improving the classification with respect to case (_ORIG_), by better isolating urban areas and closing gaps over forested areas. This is confirmed by the corresponding accuracy values in Table 5, where a positive increment of both the overall and average accuracy, \(\Delta_{\text{OA}}\) and \(\Delta_{\text{AA}}\), respectively, is detected. In particular, in patches (a) and (b), textures are helpful to better classify small details in man-made structures. This results in a slight increase of the overall accuracy (between 1.29% and 1.78%), together with a more relevant improvement in the average accuracy as well (between 2.21% and 6.02%). In patch (c), the inclusion of SADH textures provides a better segmentation of the class _forests_ (FOR) with respect to the classification results obtained using the sole interferometric and backscattering parameters. The random noise-like misclassification occurrences are reduced, resulting in an increase of both OA and AA, which varies between 1.12% and 2.10%. Finally, in patch (d) the introduction of textures also allows for correctly classifying bare soil areas as _non-forested areas_ (NFR), which would otherwise result in misclassified _artificial surfaces_ (ART). As a further comment, although this first analysis demonstrates the improvement obtained by using the SADH textures for all the considered patches, patch (a) presents lower values of accuracy with respect to the other ones even when considering case (_SADH_), with an overall and average accuracy of 73.60% and 78.15%, respectively. Such low values are mainly caused by the time difference between the Sentinel-1 acquisitions (April-May 2019) and the REF reference map from 2017. Indeed, this is confirmed by observing the True Color (RGB) and the Normalized Difference Vegetation Index (NDVI) maps from a Sentinel-2 acquisition of 16 May 2019, depicted in the last two columns of Figure 6. The RGB map shows how the city of Porto Velho, the capital of Rondonia, is more extended with respect to 2017, especially on the top-right corner of patch (a). Similar inconsistencies can also be found in patch (b) when observing both the urban and the deforested areas.