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Ship Detection and Recognition Using Hybrid Combination Algorithm

机译:混合组合算法的船舶检测与识别

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摘要

Ship detection in the satellite images is gaining the attention of researchers as it has wide range of applications such as fishery management, maritime management, traffic monitoring etc. Considering the highly variable environment and weather conditions in closely spaced areas, obtaining a very controlled false alarm rate is the major problem for a non-homogenous sea clutter. since the SAR images are less influenced by weather conditions and time they are very suitable for ship detection. In a short period, the modern SARs can generate large amounts of data which brings the need for automatic detection of targets. These target detection systems carry out the detection process in three stages: identification, discrimination and classification. In this paper, we are proposing a new method of object detection by using the shape and color analysis, followed by morphological operation and GLCM. The proposed algorithm can be used to detect the crashed aero planes, floating containers and many other objects. The common CFAR has one false alarm while the results from the Hybrid combination algorithm matches with the ground truth.
机译:卫星图像中的船舶检测由于其广泛的应用而受到了研究人员的关注,例如渔业管理,海事管理,交通监控等。考虑到紧密间隔区域中环境和天气条件的高度变化,获得了可控的错误警报速率是非均匀海杂波的主要问题。由于SAR图像受天气条件和时间的影响较小,因此非常适合船舶检测。在短时间内,现代SAR可以生成大量数据,这需要自动检测目标。这些目标检测系统分三个阶段执行检测过程:识别,区分和分类。在本文中,我们提出了一种使用形状和颜色分析,然后进行形态学运算和GLCM的新对象检测方法。所提出的算法可用于检测坠毁的飞机,浮动集装箱和许多其他物体。普通CFAR发出一个错误警报,而混合组合算法的结果与真实情况相匹配。

著录项

  • 作者单位

    Texas A&M University - Kingsville.;

  • 授予单位 Texas A&M University - Kingsville.;
  • 学科 Electrical engineering.
  • 学位 M.S.
  • 年度 2018
  • 页码 50 p.
  • 总页数 50
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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