Abstract
With the rapid development of world shipping, oil spill accidents such as tanker collisions, illegal sewage discharges, and oil pipeline ruptures occur frequently. As the SAR system expands from single polarization to multipolarization, the Polarmetric Synthetic Aperture Radar (Pol-SAR) system has been widely used in marine oil spill detection. However, in the studies of the oil spill extraction in SAR images, there are some problems that limit large-scale oil spill detection work. As a transition from single-polarized to full-polarized, the dual-polarized system carries some polarization information and can be obtained in large quantities for free, which has become a major breakthrough in solving the problem of large-scale oil spill detection. In order to optimize the multisource features that can be extracted from dual-polarized SAR images, greatly improve the utilization rate of dual-polarized SAR oil spill images under the premise of reducing workload, and ensure the accuracy of marine oil spill extraction, this paper adopts the metric of inter-class separability, the Jeffries–Matusita distance, which improves on the traditional K-means algorithm by focusing on the noise sensitivity defect of the K-means algorithm; the artificial influence of J–M distance in measuring the separability between classes improves the algorithm in three aspects: sample selection, distance calculation, and data evaluation. Finally, using the inter-sample J–M distance of multisource features, the overall accuracy of image segmentation, the F1-score, and the results of correlation analysis between features, three advantageous features and three subdominant features are selected that can be used for marine oil spill detection.
Funder
Liaoning Revitalization Talents Program
China National Key R&D Program
Subject
General Earth and Planetary Sciences
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献