复杂背景下用于小目标检测的红外超像素斑块图像模型

IF 3.1 3区 物理与天体物理 Q2 INSTRUMENTS & INSTRUMENTATION Infrared Physics & Technology Pub Date : 2024-08-22 DOI:10.1016/j.infrared.2024.105490
Jinglin Xin, Man Luo, Xinxin Cao, Teng Liu, Jiakang Yuan, Rong Liu, Yunhong Xin
{"title":"复杂背景下用于小目标检测的红外超像素斑块图像模型","authors":"Jinglin Xin,&nbsp;Man Luo,&nbsp;Xinxin Cao,&nbsp;Teng Liu,&nbsp;Jiakang Yuan,&nbsp;Rong Liu,&nbsp;Yunhong Xin","doi":"10.1016/j.infrared.2024.105490","DOIUrl":null,"url":null,"abstract":"<div><p>The main problem of infrared small target detection in complex background is how to effectively eliminate the edge residue. In this paper, we propose an efficient method named Superpixel Patch Image (SPI) model to handle this challenging task. The SPI model can fit the edges of the background well, thus effectively eliminating edge interference in the process of target detection, and achieving excellent performance. The SPI method consists of three steps: Firstly, an improved Simple Linear Iterative Clustering (ISLIC) algorithm is proposed to generate compact superpixels that perfectly match the background edge. Secondly, setting each superpixel patch as a column, a large patch-image matrix is constructed, and the target foreground image and background image is separated by imprecisely augmented Lagrange multiplication. Finally, based on the comprehensively analysis of the distribution characteristics of the target and the highlighted edge in the foreground image, an adaptive threshold is used to extract the target from the foreground superpixel patch. The experimental results of real infrared scenes show that the presented SPI model achieves the best SCRG, BSF and ROC curves compared with the existing 9 state-of-art algorithms, and can effectively extract small targets under different complex backgrounds.</p></div>","PeriodicalId":13549,"journal":{"name":"Infrared Physics & Technology","volume":"142 ","pages":"Article 105490"},"PeriodicalIF":3.1000,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Infrared superpixel patch-image model for small target detection under complex background\",\"authors\":\"Jinglin Xin,&nbsp;Man Luo,&nbsp;Xinxin Cao,&nbsp;Teng Liu,&nbsp;Jiakang Yuan,&nbsp;Rong Liu,&nbsp;Yunhong Xin\",\"doi\":\"10.1016/j.infrared.2024.105490\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The main problem of infrared small target detection in complex background is how to effectively eliminate the edge residue. In this paper, we propose an efficient method named Superpixel Patch Image (SPI) model to handle this challenging task. The SPI model can fit the edges of the background well, thus effectively eliminating edge interference in the process of target detection, and achieving excellent performance. The SPI method consists of three steps: Firstly, an improved Simple Linear Iterative Clustering (ISLIC) algorithm is proposed to generate compact superpixels that perfectly match the background edge. Secondly, setting each superpixel patch as a column, a large patch-image matrix is constructed, and the target foreground image and background image is separated by imprecisely augmented Lagrange multiplication. Finally, based on the comprehensively analysis of the distribution characteristics of the target and the highlighted edge in the foreground image, an adaptive threshold is used to extract the target from the foreground superpixel patch. The experimental results of real infrared scenes show that the presented SPI model achieves the best SCRG, BSF and ROC curves compared with the existing 9 state-of-art algorithms, and can effectively extract small targets under different complex backgrounds.</p></div>\",\"PeriodicalId\":13549,\"journal\":{\"name\":\"Infrared Physics & Technology\",\"volume\":\"142 \",\"pages\":\"Article 105490\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-08-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Infrared Physics & Technology\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1350449524003748\",\"RegionNum\":3,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"INSTRUMENTS & INSTRUMENTATION\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Infrared Physics & Technology","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1350449524003748","RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0

摘要

复杂背景下红外小目标检测的主要问题是如何有效消除边缘残留。本文提出了一种名为 "超像素补丁图像(SPI)模型 "的高效方法来处理这一具有挑战性的任务。SPI 模型能很好地拟合背景的边缘,从而有效地消除目标检测过程中的边缘干扰,取得优异的性能。SPI 方法包括三个步骤:首先,提出一种改进的简单线性迭代聚类(ISLIC)算法,生成与背景边缘完全匹配的紧凑超像素。其次,将每个超像素补丁设置为一列,构建一个大型补丁-图像矩阵,并通过不精确增强拉格朗日乘法分离目标前景图像和背景图像。最后,在综合分析目标和高亮边缘在前景图像中的分布特征的基础上,使用自适应阈值从前景超像素斑块中提取目标。真实红外场景的实验结果表明,与现有的 9 种先进算法相比,本文提出的 SPI 模型实现了最佳的 SCRG、BSF 和 ROC 曲线,能有效地提取不同复杂背景下的小目标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Infrared superpixel patch-image model for small target detection under complex background

The main problem of infrared small target detection in complex background is how to effectively eliminate the edge residue. In this paper, we propose an efficient method named Superpixel Patch Image (SPI) model to handle this challenging task. The SPI model can fit the edges of the background well, thus effectively eliminating edge interference in the process of target detection, and achieving excellent performance. The SPI method consists of three steps: Firstly, an improved Simple Linear Iterative Clustering (ISLIC) algorithm is proposed to generate compact superpixels that perfectly match the background edge. Secondly, setting each superpixel patch as a column, a large patch-image matrix is constructed, and the target foreground image and background image is separated by imprecisely augmented Lagrange multiplication. Finally, based on the comprehensively analysis of the distribution characteristics of the target and the highlighted edge in the foreground image, an adaptive threshold is used to extract the target from the foreground superpixel patch. The experimental results of real infrared scenes show that the presented SPI model achieves the best SCRG, BSF and ROC curves compared with the existing 9 state-of-art algorithms, and can effectively extract small targets under different complex backgrounds.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.70
自引率
12.10%
发文量
400
审稿时长
67 days
期刊介绍: The Journal covers the entire field of infrared physics and technology: theory, experiment, application, devices and instrumentation. Infrared'' is defined as covering the near, mid and far infrared (terahertz) regions from 0.75um (750nm) to 1mm (300GHz.) Submissions in the 300GHz to 100GHz region may be accepted at the editors discretion if their content is relevant to shorter wavelengths. Submissions must be primarily concerned with and directly relevant to this spectral region. Its core topics can be summarized as the generation, propagation and detection, of infrared radiation; the associated optics, materials and devices; and its use in all fields of science, industry, engineering and medicine. Infrared techniques occur in many different fields, notably spectroscopy and interferometry; material characterization and processing; atmospheric physics, astronomy and space research. Scientific aspects include lasers, quantum optics, quantum electronics, image processing and semiconductor physics. Some important applications are medical diagnostics and treatment, industrial inspection and environmental monitoring.
期刊最新文献
Breaking dimensional barriers in hyperspectral target detection: Atrous convolution with Gramian Angular field representations Multi-Scale convolutional neural network for finger vein recognition Temporal denoising and deep feature learning for enhanced defect detection in thermography using stacked denoising convolution autoencoder Detection of black tea fermentation quality based on optimized deep neural network and hyperspectral imaging Hyperspectral and multispectral images fusion based on pyramid swin transformer
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1