## 3 Results  
This section presents the results obtained from classifying the two images, HSI1 (30 m GSD) and HSI2 (GSD 5 m). At the beginning of each trial, different points were randomly assigned to the validation and test groups, separating them from the points used for training. Specifically, the percentages of the 938 points allocated to the three different groups are: 30% for the final test of the results, 49% for training the CNN, and 21% for validating the classification model. In this way, two confusion matrices were generated for each trial, one for validation and one for testing, from which the accuracy metrics were measured. The twenty confusion matrices generated from the five trials are included in the Supplementary Materials along with the OA, K, UA, PA, and F1 values. For brevity, this section only reports the values related to the test phase of the results, which is deemed most representative of the trials conducted. Table 2 contains the OA and the inter-rater reliability K coefficient obtained for every trial (T) relative to the test phase. The OA ranges in the results in the last row show that, on average, HSI2 achieves an accuracy value that is 0.14 points higher compared to HSI1. Similarly, the K values show an inter-rater reliability coefficient of 0.2 higher for the HSI2 image that has undergone pansharpening. UA and PA are very useful for assessing the underestimation or overestimation of individual classes [58]. They complement commission error and omission error metrics. The values of these metrics for each test in the validation phase and in the test phase are shown in the Supplementary Materials. Table 3 shows the average value of UA and PA obtained in the test phase for all the trials performed with HSI1 and for all the trials performed with HSI2. In general, it is evident that the UA and PA values increase when the HSI2 image is used. The reasons for this improvement are related to the application of the pansharpening technique and will be discussed below. One aspect that must be emphasized here is that with the use of the HSI2 image, there is a reduction in both inclusion errors (commission errors) and exclusion errors (omission errors). This is driven by a substantial increase in both UA and PA. In fact, for the classes _Pinus pinea_ and _Tilia \(\times\) europaea_, for which the PA values are slightly higher than the UA values, the HSI2 image makes it possible to reduce the commission error margin, thus reducing the overestimation. For the other classes, where the UA values are greater than the PA values and thus underestimation occurs, a reduction in the omission error is obtained compared to the use of HSI1. The classes _Pinus pinea_ and _Tilia \(\times\) europaea_ are slightly overestimated, while all the other classes are underestimated, using both HSI1 and HSI2. This phenomenon is probably related to the number of GTs, which is higher for _Pinus pinea_ and _Tilia \(\times\) europaea_, allowing a higher representativeness and avoiding the omission error. Table 4 shows the results in terms of F1 related to the test phase of the five classification trials (T) conducted using the two images, HSI1 and HSI2. Furthermore, the table shows the average F1 obtained per class, along with the range that indicates the improvement generated by the use of pansharpening. The improved accuracy of the results demonstrates the effectiveness of pansharpening. With the higher resolution of the HSI2 image, the classifier can more accurately identify the spectral signature of tree species and assign pixels to the correct class. In contrast, the lower resolution of the HSI1 image makes this task more challenging, complicating the classification process. The pansharpening method makes it possible to emphasize the spectral characteristics of surfaces, even if only in the panchromatic range, by sharpening the values contained in the lower resolution pixels without altering the shape of the spectral signatures' curve.