基于SAR图像数据的溢油检测用于海洋污染远程监测的轻量化图像实现

K. Vyas, Pooja Shah, Usha Patel, T. Zaveri, Raj Kumar
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引用次数: 7

摘要

海洋生物油和矿物油泄漏会对海洋生态系统造成严重损害。以往的研究表明,对合成孔径雷达(SAR)数据生成的图像进行图像处理是最合适的溢油远程检测方法。本文从溢油检测的三个步骤,即黑点检测、特征提取和分类,阐述溢油检测的框架。本文重点研究了迟滞算法对黑点的分割和决策树对黑点和相似点的分类。溢油检测的主要步骤已在imageJ中实现,它为上述应用程序的现有软件提供了轻量级解决方案。
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Oil spill detection from SAR image data for remote monitoring of marine pollution using light weight imageJ implementation
Biogenic and Mineral oil spill in the ocean can harm the marine ecosystem significantly. Past research suggests image processing on images generated from Synthetic Aperture Radar (SAR) data are the most suitable way of remote oil spill detection. In this paper, the framework for oil spill detection is explained in three step process for oil spill detection viz., dark spot detection, feature extraction, and classification. The paper focuses on Hysteresis algorithm for segmenting the dark spots and decision tree for the classification of dark spots and look alikes. The major steps in oil spill detection have been implemented in imageJ which gives a light weight solution to existing softwares for the said application.
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