Xi'an Institute of Optics and Precision Mechanics,CAS
Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection | |
Huang, Ju1,2; Liu, Kang1,2; Xu, Mingliang3; Perc, Matjaz4; Li, Xuelong5,6 | |
作者部门 | 海洋光学技术研究室 |
2021 | |
发表期刊 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING |
ISSN | 1939-1404;2151-1535 |
卷号 | 14页码:8113-8124 |
产权排序 | 1 |
摘要 | Hyperspectral anomaly detection has attracted extensive interests for its wide use in military and civilian fields, and three main categories of detection methods have been developed successively over past few decades, including statistical model-based, representation-based, and deep-learning-based methods. Most of these algorithms are essentially trying to construct proper background profiles, which describe the characteristics of background and then identify the pixels that do not conform to the profiles as anomalies. Apparently, the crucial issue is how to build an accurate background profile; however, the background profiles constructed by existing methods are not accurate enough. In this article, a novel and universal background purification framework with extended morphological attribute profiles is proposed. It explores the spatial characteristic of image and removes suspect anomaly pixels from the image to obtain a purified background. Moreover, three detectors with this framework covering different categories are also developed. The experiments implemented on four real hyperspectral images demonstrate that the background purification framework is effective, universal, and suitable. Furthermore, compared with other popular algorithms, the detectors with the framework perform well in terms of accuracy and efficiency. |
关键词 | Anomaly detection background purification extended attribute profile (EAP) hyperspectral image (HSI) sparse representation (SR) stacked autoencoder (SAE) |
DOI | 10.1109/JSTARS.2021.3103858 |
收录类别 | SCI |
语种 | 英语 |
WOS记录号 | WOS:000690441600011 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.opt.ac.cn/handle/181661/95033 |
专题 | 海洋光学技术研究室 |
通讯作者 | Li, Xuelong |
作者单位 | 1.Chinese Acad Sci, Xian Inst Opt & Precis Mech, Shaanxi Key Lab Ocean Opt, Xian 710119, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Zhengzhou Univ, Sch Informat Engn, Zhengzhou 450001, Peoples R China 4.Univ Maribor, Fac Nat Sci & Math, Maribor 2000, Slovenia 5.Northwestern Polytech Univ, Sch Artificial Intelligence Opt & Elect, Xian 710072, Peoples R China 6.Northwestern Polytech Univ, Minist Ind & Informat Technol, Key Lab Intelligent Interact & Applicat, Xian 710072, Peoples R China |
推荐引用方式 GB/T 7714 | Huang, Ju,Liu, Kang,Xu, Mingliang,et al. Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2021,14:8113-8124. |
APA | Huang, Ju,Liu, Kang,Xu, Mingliang,Perc, Matjaz,&Li, Xuelong.(2021).Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,14,8113-8124. |
MLA | Huang, Ju,et al."Background Purification Framework With Extended Morphological Attribute Profile for Hyperspectral Anomaly Detection".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 14(2021):8113-8124. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Background Purificat(5483KB) | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | 浏览 请求全文 |
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