## 6 Evaluation and Validation  
To evaluate the accuracy of oil spill detection from multi-source satellite data based on the proposed algorithm, we utilize the quantitative metrics, including Precision, Recall, and F1-score, defined as follows:  
\[\text{Precision}=\frac{TP}{TP+FP}\]  
\[\text{Recall}=\frac{TP}{TP+FN}\]  
Figure 17: Surface wind and current speed and direction (indicated by the arrows), corresponding to the extracted Sentinel-1 IW, EW, and ICEYE-X scenes (Figure 16a–c), respectively. **(i)** ERA-5 wind vectors **(a)** 01:00, 10 August 2020, and **(b)** 14:00 and **(c)** 11:00, 11 August. **(ii)** CMEMS current vectors on **(d)** 01:30, 10 August, and **(e)** 14:30 and **(f)** 11:30, 11 August. **(iii)** Mean values of wind and current fields from 01:00, 10 August, to 11:00, 11 August. \[\text{F1}=2\times\frac{\text{Precision}\times\text{Recall}}{\text{Precision}+\text{Recall}}\]  
_TP_, _FP_, and _FN_ stand for True Positive, False Positive, and False Negative, respectively. In the context of oil spill detection, Precision assesses the algorithm's ability to differentiate between actual oil slicks and look-alikes. Recall, on the other hand, measures the algorithm's effectiveness in identifying all oil pixels, ensuring none are missed. F1-score is the harmonic mean of the Precision and Recall. A perfect model has an F1-score of 1. The ground truth has been determined through visual assessment of the available dataset. Table 3 shows the Precision, Recall, and F1-score of the proposed method for oil spill detection from different satellite images, for the three cases in the Persian Gulf (Q#1-2-3). The Precision rates are quite high for most cases, meaning that our algorithm can accurately distinguish oil objects from look-alikes, while the Recall shows lower values (Case Q#1 and Q#2), especially for oil spill detection from optical images (Sentinel-2/3 and Landsat-8), meaning that the algorithm misses detecting some small oil objects, especially from optical images. However, the F1-scores of the three cases are satisfactory, especially for Case Q#3. To highlight the advantages of the proposed two-step algorithm, Figure 18i,ii compares the oil slicks identified from the Sentinel-2 (scene S#3-Figure 13c) and Sentinel-1 (scene S#2-Figure 13a) images, respectively, for Case Q#3, 3 September 2021, by only HSBA (Figure 18i-a, Figure 18 ii-d), those identified from HSBA plus oil contour (Figure 18i-b, Figure 18 ii-e), and those manually segmented (Figure 18i-c, Figure 18 ii-f). HSBA exhibited misclassifications between oil objects and look-alikes in both instances when compared to manually identified oil slicks. Specifically, the precision of the HSBA-based approach is only 6.7% for the Sentinel-2 image (Figure 18i-a), primarily due to a significant number of false positives, and approximately 55.15% for the Sentinel-1 image (Figure 18 ii-d), also owing to a notable presence of false positives. However, in the two-step approach, most look-alikes are eliminated, resulting in detected oil slicks (Figure 18i-b, Figure 18 ii-e) closely resembling those manually segmented (Figure 18i-c, Figure 18 ii-f) for both Sentinel-1 and Sentinel-2 images. As a result, the precision scores are significantly improved, reaching 96.15% for Sentinel-2 and 93.55% for Sentinel-1. The Recall score values, as shown in Table 3, are lower due to the missing of some oil pixels detected in comparison to the manually segmented ones. Figures 18 and 19 compare the oil slicks detected by the algorithm against those manually segmented from Sentinel-2 imagery on 5 July 2021, at 06:56:21 (Figure 19), and Landsat-8 imagery on 6 July 2021, at 06:58:26 (Figure 20). The proposed algorithm successfully identifies all oil objects on both Sentinel-2 and Landsat-8 images. However, it fails to detect some oil pixels when the two figures are superimposed (Figures 19c and 20c). Specifically, the Recall scores of the proposed algorithm for oil-slick detection are 58.87% for the Sentinel-2 image (Figure 19) and 37.88% for the Landsat-8 image (Figure 20).