## 7 Discussion  
The oil spill detection algorithm presented in this paper is a two-step procedure. The strength of this approach is that it can be applied to different parameters extracted from different satellite images, i.e., SAR NRCS, optical oil index, and T865 variable. Its flexibility is particularly useful when processing new datasets since it is fully automatic and adaptive. In the first step, HSBA was applied to the input variables used to identify oil slicks, observed as dark objects on Sentinel-1/3 images and dark and bright entities on Sentinel-2 and Landsat-8 images). As shown in Figure 18, HSBA is generally effective in accurately delineating oil objects when they exhibit significant differences from the sea surface background. However, it may encounter challenges in correctly classifying some look-alikes that have pixel values similar to those of oil objects. To address this, in the second step, we employed a method for determining oil contours by applying non-linear filters (mean and standard deviation) along with an adaptive thresholding approach. This technique is grounded in the hypothesis that oil objects possess a more uniform structure than look-alikes due to oil viscosity, making them better separated from sea clusters. This distinction is particularly evident in the Sentinel-2 image (Figure 13c, middle scene), in which the oil contour enables the differentiation between look-alikes and actual oil objects. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Case** & **Sensor** & **Precision** & **Recall** & **F1-Score** \\ \hline \multirow{3}{*}{Mauritius 10–11 August 2020} & Sentinel-1 IW & 0.90 & 0.862 & 0.88 \\ \cline{2-5}  & Sentinel-1 EW & 0.438 & 0.98 & 0.609 \\ \cline{2-5}  & ICEYE-X & 0.851 & 0.814 & 0.832 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Quantitative evaluation of the proposed algorithm’s performance (HSBA combined with oil contour determination) for oil spill detection off the shore of Mauritius vs. manually segmented ground truth data. Figure 20: Comparison between the oil slicks (**a**) detected by HSBA plus oil contour and (**b**) those manually segmented, from the Landsat-8 image, 6 July 2021, 06:58:26. (**c**) Difference between the detected pixels (**a**) and ground truth (**b**). The results of oil-slick detection in Figure 7 highlighted the advantages of the two-step procedure technique proposed in this paper, especially in cases with surface wind speeds exceeding 7 m/s, which are not optimal for oil spill detection. In instances of strong surface winds (Figure 9), the damping of the oil slicks observed in Figure 7 was less pronounced, resulting in oil objects with inhomogeneous characteristics and a notable difference in NRCS. If only the determination of oil-slick contours were to be applied, this issue would significantly impact the identification of the shapes of oil objects. However, with the application of HSBA, which selectively chooses oil objects that are clearly distinguishable as fitting the PDF of the target class used for classification over the entire image, the oil objects were accurately identified, even in cases where the objects did not have a homogeneous structure. Table 5 offers an overview of the oil detected from Sentinel-1/2/3 and Landsat-8 images off the shore of Qatar, and its movement for 4, 8, and 24 h of observations. The detected oil encompasses various scales, ranging from large to medium and small oil objects. As discussed earlier, the factors influencing oil drift include surface wind and current, as well as characteristics of the oil itself, including shape, size, and features (thin, thick, homogeneous, etc.). Table 5 shows that the oil drift may move in the same direction as wind and/or current, depending on wind/current velocity, as well as the difference or similarity of the wind and current directions. In Case #Q1, accurately estimating the movement direction of the large-scale oil is challenging due to significant changes in the velocities and directions of wind and current after 24 h (Figure 9).