## 3 Results
### Cross-Validation and Validation  
Table 3 summarizes the cross-validation and validation classification accuracies. Several outcomes can be drawn from this table. First, the validation metrics are naturally lower than the cross-validation ones. Yet, the differences between the values are relatively small, which indicates a robust model. Second, the two sensors without bands in the SWIR spectral region (i.e., VENUS and CASI) have the lowest accuracy. Third, the hyperspectral sensors have superior results over the multispectral, with the prominent exception  
Figure 5: Optimizing the probability threshold for each sensor. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sensor.**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**Best**} & \multicolumn{2}{c}{**Overall Accuracy**} & \multicolumn{2}{c}{**Sensitivity**} & \multicolumn{2}{c}{**Specificity**} & \multicolumn{2}{c}{**Kappa**} \\ \cline{3-11}  & & & **Threshold** & **Cal** & **Val** & **Cal** & **Val** & **Cal** & **Val** & **Cal** & **Val** \\ \hline Fenix & Hyperspectral & 72 & 0.95 & 0.93 & 0.95 & 0.91 & 0.96 & 0.94 & 0.90 & 0.85 \\ EnMap & Hyperspectral & 98 & 0.97 & 0.93 & 0.97 & 0.93 & 0.98 & 0.94 & 0.94 & 0.85 \\ Hyperion & Hyperspectral & 73 & 0.97 & 0.92 & 0.97 & 0.91 & 0.97 & 0.92 & 0.93 & 0.82 \\ AVIRIS & Hyperspectral & 69 & 0.97 & 0.94 & 0.97 & 0.91 & 0.97 & 0.95 & 0.94 & 0.86 \\ HyMap & Hyperspectral & 85 & 0.96 & 0.92 & 0.96 & 0.90 & 0.96 & 0.94 & 0.91 & 0.83 \\ CASI & Hyperspectral & 73 & 0.63 & 0.50 & 0.62 & 0.69 & 0.63 & 0.40 & 0.23 & 0.08 \\ SN2 & Multispectral & 63 & 0.90 & 0.80 & 0.90 & 0.88 & 0.90 & 0.76 & 0.78 & 0.59 \\ LS8 & Multispectral & 50 & 0.87 & 0.75 & 0.87 & 0.76 & 0.87 & 0.74 & 0.73 & 0.47 \\ VENUS & Multispectral & 72 & 0.64 & 0.72 & 0.64 & 0.65 & 0.64 & 0.76 & 0.26 & 0.40 \\ ASTER & Multispectral & 43 & 0.90 & 0.76 & 0.90 & 0.66 & 0.90 & 0.80 & 0.78 & 0.46 \\ WV3 & Multispectral & 79 & 0.97 & 0.95 & 0.97 & 0.93 & 0.97 & 0.96 & 0.94 & 0.89 \\ Self & Multispectral & 38 & 0.91 & 0.84 & 0.91 & 0.77 & 0.91 & 0.87 & 0.80 & 0.64 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Cross validation (Cal) and validation (Val) results from the spectral resample simulations. of WV3. Fourth, the Fenix sensor, with the highest number of spectral bands (i.e., has the most spectral information), does not stand out in terms of accuracy metrics. Fifth, all the hyperspectral sensors (and WV3) scores are closely akin. Sixth, WV3 (multispectral in the VNIR-SWIR) exhibits better results in comparison to its multispectral counterparts in the VNIR-SWIR (i.e., SN2, LS8, and ASTER). This is probably due to its spectral bands which are adequately located to \"harvest\" the important and diagnostic spectral information for the calibration set. Nevertheless, it is important to remember that this is a simulation, based on spectral resampling (where the other characteristics are of the Fenix sensor) aiming at demonstrating the potential of different sensors to perform well in a study site such as ours (see further Figure 6 for an example). Therefore, the results here should be interpreted as such. For example, the AVIRIS Kappa score in the validation is higher than the Fenix (0.85 versus 0.86). It does not mean that in practice AVIRIS will necessarily yield better results, rather that they have similar potential to achieve good classification results. Likewise, WV3 should not be considered to outperform the hyperspectral sensors (even if the exact numbers indicate that), rather to suggests its great potential of performing well in detecting PHC contamination as shown by [6]. Figure 6 shows the Kappa coefficient for all of the sensors in all of the added noise levels. The presented data correspond to the validation dataset (100 oil and 200 clean pixels). Almost all of the sensors have a better Kappa score with decreasing added noise, excluding CASI, which has especially low scores (see also Table 3) and thus can be considered unstable. Similarly, ASTER seems insensitive to added noise, but again, with low accuracy results.