# Assessing the Potential of PlanetScope Imagery for Iron Oxide Detection in Antimony Exploration
## 5 Discussion
### Br  
The results show that PS is the most sensitive in identifying pixels in the most important classes (classes 15 and 16), followed by Sentinel-2, while Landsat 9 proved to be the least sensitive, as it did not present significant results for class 16. ASTER performed distinctly, effectively identifying pixels in classes 13 and 14; however, upon further analysis, we observed that this high detection, especially in class 14, may indicate the presence of false positives associated with vegetation. In the results obtained for reflectance bands (RBs) of PS and Sentinel-2, classes 14, 15, and 16 have low values, but the pixels are distributed across areas of interest. Conversely, for ASTER and Landsat 9, the pixels are concentrated in class 14 rather than being distributed in classes 15 and 16, resulting in the formation of large clusters within a single class, which are not intuitive for identifying areas of interest.  
We observed that the higher the spatial resolution, the greater the sensitivity in identifying pixels in the higher classes (15 and 16). Although ASTER and Landsat 9 identified  
Figure 10: Spatial context of ROIs about fault zones. (**a**) Overview map of the study area. (**b**) ROI 2 is located directly on a fault line. (**c**) ROI 4 is positioned approximately 470 m from a fault, suggesting potential structural influence on mineralization patterns.  
pixels in medium-value classes (class 13) and high-value classes (class 14), these pixels were highly concentrated, forming large clusters that were not intuitive for prospectivity analysis. PS stands out overwhelmingly compared to the other satellites in detecting maximum-value pixels (class 16), the most important class for identifying iron oxides, highlighting its great potential for prospecting studies.  
False positives related to non-vegetated areas were primarily observed in the BR 2/1 of ASTER, which misclassified extensive regions with sparse vegetation as class 14. Among all satellites, ASTER was the only one that consistently confused vegetated areas with areas of interest. When analyzing the NDVI results (Figure A4, Appendix A), we observed that ASTER detected significantly less vegetation, especially in the central region of the image. By comparing the NDVI with a true-color image from the same year on Google Earth, we confirmed that this area is predominantly covered by sparse vegetation, which was not correctly identified by ASTER's NDVI. Comparative studies have shown that ASTER's NDVI tends to yield lower values compared to other sensors [47]. Although similar band ratios were applied to other satellites, they did not show the same level of misclassification, suggesting that ASTER's spectral response in this configuration is less effective at distinguishing subtle vegetation signals from background materials such as iron oxides.  
Landsat 9 also presented false positives with vegetation, but in medium-value classes (12 and 13), which did not significantly affect the analysis compared to ASTER. PS and Sentinel-2 showed no false positives with vegetation, as in both cases, vegetation was identified with low values, ensuring greater reliability in detecting iron oxide occurrences.