A method for feature extraction and classification of marine radar images

Chihiro Nishizaki, Y. Niwa, Motonobu Imasato, Hisaya Motogi
{"title":"A method for feature extraction and classification of marine radar images","authors":"Chihiro Nishizaki, Y. Niwa, Motonobu Imasato, Hisaya Motogi","doi":"10.1109/WAC.2014.6935652","DOIUrl":null,"url":null,"abstract":"There are ship images (target) and non-ship images (noise) on radar images. In order to obtain other ship information from radar images, it is necessary to select and acquire ship images on radar images. Ship images are selected and acquired by navigation officers based on their observation skill and experience. The future purpose of this study is to automatically detect ship images on the radar. Therefore, in this paper, we propose the method for dividing ship images from non-ship images by the image processing and the cluster analysis using radar raster images. Many image feature points were extracted by the image processing using radar raster images. As a result of the cluster analysis using these image feature points, it is possible to detect about 99.8% ship images from radar raster images. However, there were many cases that non-ship images were classed as ship images. Therefore, the accuracy rate of cluster analysis results in this study was about 83%. In other words, it was possible to fairly determine about 83% images in this study.","PeriodicalId":196519,"journal":{"name":"2014 World Automation Congress (WAC)","volume":"68 3","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2014 World Automation Congress (WAC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/WAC.2014.6935652","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 3

Abstract

There are ship images (target) and non-ship images (noise) on radar images. In order to obtain other ship information from radar images, it is necessary to select and acquire ship images on radar images. Ship images are selected and acquired by navigation officers based on their observation skill and experience. The future purpose of this study is to automatically detect ship images on the radar. Therefore, in this paper, we propose the method for dividing ship images from non-ship images by the image processing and the cluster analysis using radar raster images. Many image feature points were extracted by the image processing using radar raster images. As a result of the cluster analysis using these image feature points, it is possible to detect about 99.8% ship images from radar raster images. However, there were many cases that non-ship images were classed as ship images. Therefore, the accuracy rate of cluster analysis results in this study was about 83%. In other words, it was possible to fairly determine about 83% images in this study.
一种海洋雷达图像特征提取与分类方法
雷达图像上有舰船图像(目标)和非舰船图像(噪声)。为了从雷达图像中获取船舶的其他信息,需要在雷达图像上对船舶图像进行选择和获取。船舶图像是由航海员根据自己的观测技能和经验来选择和获取的。本研究的未来目标是在雷达上自动检测船舶图像。因此,本文提出了利用雷达光栅图像进行图像处理和聚类分析,将舰船图像与非舰船图像进行区分的方法。利用雷达光栅图像对图像进行处理,提取了大量的图像特征点。利用这些图像特征点进行聚类分析的结果是,可以从雷达光栅图像中检测出约99.8%的船舶图像。然而,在许多情况下,将非船舶图像归类为船舶图像。因此,本研究的聚类分析结果准确率约为83%。换句话说,在这项研究中,可以公平地确定大约83%的图像。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信