Oil Spill Identification with Marine Radar Using Feature Augmentation and Improved Firefly Optimization Algorithm

Oil spill accidents pose a grave threat to marine ecosystems, human economy, and public health. Consequently, expeditious and efficacious oil spill detection technology is imperative for the pollution mitigation and the health preservation in the marine environment. This study proposed a marine radar oil spill detection method based on Local Binary Patterns (LBP), Histogram of Oriented Gradient (HOG), and an improved Firefly Optimization Algorithm (IFA). In the stage of image pre-processing, the oil film features were significantly enhanced through three steps. The LBP features were extracted from the preprocessed image. Then, the mean filtering was used to smooth out the LBP features. Subsequently, the HOG statistical features were extracted from the filtered LBP feature map. After the feature enhancement, the oil spill regions were accurately extracted by using K-Means clustering algorithm. Next, an IFA model was used to classify oil films. Compared with traditional Firefly Optimization Algorithm (FA) algorithm, the IFA method is suitable for oil film segmentation tasks in marine radar data. The proposed method can achieve accuracy segmentation and provide a new technical path for marine oil spill monitoring.