# Detection of Marine Oil Spill from PlanetScope Images Using CNN and Transformer Models
## 6 Conclusions  
While SAR images are primarily used for oil spill detection, combining SAR and optical images could improve the spatial and temporal coverage of oil spill detection. In this context, this paper introduces a new approach to assess the feasibility of optical satellite images for marine oil spill detection. This study utilized high-resolution optical satellite imagery from PlanetScope to perform marine oil spill detection using DeepLabV3+, Swin-UPerNet, and Mask2 Former, representing CNN and Transformer models, respectively. We conducted five blind tests to objectively compare and evaluate the applicability of these models. Additionally, we were able to classify the type of oil by analyzing the histogram of the oil spill pixels predicted by the deep learning models. A total of 260 images with four channels and 256 \(\times\) 256 pixel dimensions were augmented to 2600 images for training and evaluation. Overall, the results suggest that both the CNN and Transformer models can detect oil spills with high accuracy, but the Transformer models (Swin-UPerNet and Mask2 Former) outperformed the CNN model (DeepLabV3+). Particularly, Swin-UPerNet consistently demonstrated the highest performance, attributed to its shifted-window self-attention mechanism, pyramid pooling for multi-scale feature integration, and head structure for detailed binary delineation. Furthermore, Transformer models are particularly effective in detecting oil spills with complex, irregular shapes, as their ability to preserve fine edge details makes them more adept at delineating the intricate forms of oil spills. To address the limitations of dataset volume, future research should incorporate additional datasets and explore novel image augmentation techniques using generative adversarial networks (GANs). These efforts could further enhance the performance of deep learning models for marine oil spill detection. Future work will be needed to develop a robust segmentation model using a multi-model ensemble with automated hyperparameter optimization. Additionally, the combination of SAR and optical images should be explored to ensure broader spatial and temporal coverage for oil spill detection.  
Conceptualization, J.K. and Y.L.; methodology, J.K. and Y.L.; formal analysis, J.K.; data curation, J.K.; writing--original draft preparation, J.K.; writing--review and editing, J.K., C.Y., J.Y. and Y.L.; project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the \"Monitoring System of Spilled Oils Using Multiple Remote Sensing Techniques\" funded by the Korea Coast Guard, Korea. This research was supported by a grant (2021-MOIS37-002) of the Intelligent Technology Development Program on Disaster Response and Emergency Management funded by the Ministry of Interior and Safety (MOIS, the Republic of Korea).  
Not applicable.  
Data Availability StatementData are contained within the article.  
Author [PERSON] was employed by the company SE Lab Incorporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.