Oil spill detection (OSD) in marine areas is an application of synthetic aperture radar (SAR) images to protect aquatic life. In this paper, a new oil spill detection algorithm based on levelsetmethod (LSM) is prese...
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Oil spill detection (OSD) in marine areas is an application of synthetic aperture radar (SAR) images to protect aquatic life. In this paper, a new oil spill detection algorithm based on levelsetmethod (LSM) is presented. Dark spot detection, feature extraction, and classification are the main steps in the proposed method. In the first step, a new dark spot detection method in SAR images is presented, which introduces a combination of multi-objective grey wolf optimization (MOGWO) and K-means clustering to find the best threshold level for image segmentation. This method overcomes the K-means clustering by choosing optimal number of clusters and centers. In the feature extraction step, a vector consisting of 45 arrays of calculated Legendre moments in the main, gradient and radon transform of the image is used. The oil-suspicious areas are also discriminated by the supported vector machine (SVM) classifier, and their boundaries are applied as an initial contour to a new hierarchical region-based level-set method (HRLSM). Experiments are performed on the images acquired by Envisat, UAVSAR, TerraSAR-X, and Sentinel1 satellites. The results showed the reliability and robustness of the proposed method with high accuracy, even for noisy images with heterogeneous and weak boundaries.
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