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Oil spill discrimination of multi-time-domain shipborne radar images using active contour model

机译:使用主动轮廓模型的多时域船载雷达图像的漏油泄漏

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Accidental oil spills cause serious pollution to the ocean and are difficult to control in short time. It is an important guarantee for emergency disposal to effectively monitor oil spills. Remote sensing is the main means to monitor oil spills. High false alarm rate has been an important bottleneck of this technology. In this paper, a multi-time-domain shipborne radar images discrimination mechanism was proposed. Based on the improved Sobel operator, Otsu and linear interpolation, the co-frequency interference noises were detected and suppressed. Gray intensity correction model (GICM) and dual-threshold method were used to eliminate highlighted continuous pixels. Oil films were extracted by using an active contour model (ACM). Finally, a multi-time-domain discrimination mechanism based on variation range tolerance of identified oil films centroids was designed to reduce the false alarm rate. It can provide technical support for decision-making and emergency response.
机译:意外漏油机对海洋造成严重污染,在短时间内难以控制。这是紧急处理有效监测漏油的重要保证。遥感是监控漏油的主要手段。高误报率是这项技术的重要瓶颈。本文提出了一种多时域船载雷达图像辨别机制。基于改进的Sobel运算符,OTSU和线性插值,检测和抑制了共频干扰噪声。灰色强度校正模型(GICM)和双阈值方法用于消除突出显示的连续像素。通过使用活性轮廓模型(ACM)提取油膜。最后,设计了基于所识别的油膜质心的变化范围公差的多时域辨别机制,以降低误报率。它可以为决策和应急响应提供技术支持。

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