## 3 Results  
### Spectral Bands Selection  
Figure 4 shows the F statistic for each band per sensor.  
When examining the plots in Figure 4, one must pay attention to the values in the axes as they are different between sensors. For example, the F statistic values range from 0-200 (i.e., poor separation) for CASI, whereas for AVIRIS, these values range between 0-5000. The hyper  
Figure 4: Analysis of variance (ANOVA) test results for all of the sensors. Three spectral regions, which are above the F statistic threshold, marked with red circles in the Fenix plot and denoted as 1, 2, and 3, correspond to petroleum hydrocarbons (PHC), clay minerals, and CaCO\({}_{3}\) spectral features, respectively. Please note that the x-axis represents the specific wavelengths for each sensor and pay attention to the F statistic values in each plot.  
Fenix, EnMap, HyMap, AVIRIS, and Hyperion) demonstrate similar behavior, where three spectral areas can be distinguished as good separators, specifically, \(\sim\)1250, 1730, and 2300 nm. These spectral ranges are associated with the spectral features of PHC and CaCO\({}_{3}\). The CASI sensor, which is also considered hyperspectral, does not contain information in these spectral regions. However, according to its ANOVA test, two alternative spectral regions (\(\sim\)550 and 750 nm) contain bands that are better separators than the other bands. Nevertheless, CASI's F statistic values are low, which question its ability to perform well when applying to the entire image. The multispectral sensors show similarities and dissimilarities in comparison to the hyperspectral sensors. On the one hand, the SN2 and LS8 display bands in the SWIR as more important than in the other spectral regions. ASTER and WV3 demonstrate similar behavior and indicate the \(\sim\)1700 nm region to be the most diagnostic region, and generally, WV3 has an affinity to the hyperspectral sensors. On the other hand, the 1700 nm region in Self seems to be the least important area compared to the other two spectral regions of PHC (i.e., 1200 and 2300 nm). This can be attributed to the spectral characteristics of Self. It has indeed spectral bands around the PHC-SF and therefore expected to perform similarly to ASTER and WV3 in terms of the ANOVA test. However, this is not exactly the case. A reason for that can be that its spectral bands are not perfectly located across the spectral range, in our case, and they do not completely overlap the spectral bands of ASTER and WV3. Because of that Self probably does not capture the most important spectral information, and hence, shows intermediate results (in terms of ANOVA) and different behavior from WV3.  
VENUS, which only has spectral information up to 900 nm, exhibits two small peaks (\(\sim\)600 and 750 nm, uncompelling in terms of F statistic) similarly to CASI, which covers the same spectral range.  
In the methodology described in [7], a threshold of F statistic = 1500 was set in order to decide for each band whether it was used or omitted from further analysis, to reduce the number of bands for further analysis (the main idea behind this step). The value of 1500 was originally selected because it captures peaks of the F statistic curve, allowing us to \"cherry-pick\" the good separators. However, this value was chosen for a hyperspectral sensor across the VNIR-SWIR, and here we had various sensors. If the same threshold was to apply to all sensors, then SN2, LS8, VENUS, and CASI would have zero bands left while ASTER, WV3, and Self would have one or two bands left for the analysis. In practice, applying a threshold of 1500 to all sensors would have left us only with hyperspectral sensors, which was not the intention as the merit of multispectral was also part of this study. Therefore, we decided that the 1500 threshold remained for the hyperspectral sensors excluding CASI (which covers a smaller spectral range), that had a threshold of 25 to capture its two F statistic peaks. No threshold was applied to the multispectral sensors.