# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 5 Discussion
The IoU for the oil spill class was 0.608 for DeepLabV3+, 0.758 for Swin-UPerNet, and 0.704 for Mask2 Former, while the IoU for the non-oil class was 0.873 for DeepLabV3+, 0.923 for Swin-UPerNet, and 0.904 for Mask2 Former. This difference is likely due to the challenge of obtaining sufficient training data for marine oil spills. Although DeepLabV3+ does not perform as well as Swin-UPerNet and Mask2 Former across multiple performance metrics, all the models could benefit from training on image datasets with a broader spatiotemporal range, enabling them to learn a wider variety of oil spill patterns. The superior performance metrics of Swin-UPerNet and Mask2 Former, both utilizing the Swin Transformer backbone, highlight their advanced adaptability to diverse oil spill scenarios. This suggests that optimizing model architectures for specific environmental conditions could further enhance their effectiveness. Additionally, providing more detailed information on oil types could further improve model performance. Future works should also explore more state-of-the-art (SOTA) models beyond Swin-UPerNet and Mask2 Former and consider using an ensemble of multiple models. Moreover, experiments were conducted on color characteristics and oil-type classification through a histogram analysis of spilled oil pixels. Figures 10-12 show the histogram distribution graphs and box plots of the oil spill pixels extracted from the labels, DeepLabV3+, Swin-UPerNet, and Mask2 Former, respectively. In the histogram graphs, the \(x\)-axis represents the pixel value, and the \(y\)-axis represents the number of pixels. In the box plots, the \(x\)-axis indicates the pixel value, and the \(y\)-axis indicates the RGB band. The histogram and box plot for Figure 10 show that the red, green, and blue band values are all low and clustered together, with little variation. In contrast, the histograms and box plots for Figures 11 and 12 show a higher distribution of pixel values in the red, green, and blue bands compared to Figure 10. The histograms for the red, green, and blue bands in Figure 11 almost overlap, while those in Figure 12 are somewhat misaligned. This suggests that the oil in Figure 10, with relatively low RGB pixel values, is close to dark black, while the oil in Figures 11 and 12, with relatively high RGB pixel values, has a bright silver or rainbow color. The histograms and box plots for the same cases exhibit similar patterns. Thick oil layers are often brown or black, while thin oil layers appear silver [21]. Based on this, it is inferred that the oil in Figure 10 represents a thick oil layer, while the oils in Figures 11 and 12 can be thin oil layers. The rainbow colors in the thin oil layers are caused by light interference, where light reflected from the top and bottom surfaces of the oil film interferes with each other. Additionally, oil refracts light differently depending on the wavelength, resulting in various colors depending on the viewing angle. The thin oil layers in Figures 11 and 12 exhibit a silver or rainbow color, with Figure 11 being closer to a silver tone and Figure 12 showing a more remarkable rainbow hue. This difference is reflected in the histograms. In Figure 11 of the silver tone, the histograms of the red, green,  
Figure 10: Thick oil layers with a dark black tone: histogram distribution graph and box plot of oil spill pixels extracted from the labels, DeepLabV3+, Swin-UPerNet, and Mask2 Former. The \(x\)-axis values represent the digital numbers (DNs) from PlanetScope images. (**a**) Oil mask, (**b**) histogram, and (**c**) box plot. and blue bands almost overlap, while, in Figure 12 of the rainbow tone, the histograms are somewhat misaligned. These oil spill color characteristics can be classified into (1) thick oil layers with a dark black tone, (2) thin oil layers with a bright silver tone, and (3) thin oil layers with a bright rainbow tone, based on the histogram analysis.