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K-means adaptive 2DSSA based on sparse representation model for hyperspectral target detection

文献类型：期刊论文

作者Zhou, Tianshu3; Cen, Yi1; He, Jiani2; Wang, Yueming3,4
刊名INFRARED PHYSICS & TECHNOLOGY
出版日期2024-12-01
卷号143页码:12
关键词KSSA SRBBH Joint spatial-spectral target detection Dictionary construction strategy
ISSN号1350-4495
DOI10.1016/j.infrared.2024.105616
产权排序3
英文摘要Target detection is a hot spot in hyperspectral imagery (HSI) processing. The detection accuracy of target detection algorithms based on sparse representation (SR) models usually suffers from the high reconstruction residuals caused by inaccurate background estimations and insufficient target samples. Besides, with the development of hyperspectral imaging technology, the spatial resolution of HSI has been continuously enhanced, which can provide more spatial information for target detection. However, spatial information is often overlooked, leading to the underutilization of the pluralistic features of HSI. Target detection using only spectral information is susceptible to spectral variation, resulting in a high false alarm rate. To alleviate these problems, this paper proposes a joint spatial-spectral algorithm. In terms of spectra, a dictionary construction strategy (DCS) is designed for the sparse representation-based binary hypothesis (SRBBH) detector to reduce reconstruction residuals of target and background samples. In terms of space, k-means 2D adaptive singular spectrum analysis (KSSA) is used to extract spatial features in cluster units. Using spatial features can enhance the robustness of the algorithm to spectral variation, thereby reducing false alarms. The target detection results are obtained by applying DCS-SRBBH to the KSSA feature image. We evaluate the proposed algorithm on three datasets: two public and one of our own. Comprehensive experimental results indicate that the proposed algorithm outperforms other target detection algorithms in terms of accuracy.
WOS关键词ORTHOGONAL SUBSPACE PROJECTION ; FEATURE-EXTRACTION
资助项目National Civil Aerospace Project of China[D040102]
WOS研究方向Instruments & Instrumentation ; Optics ; Physics
语种英语
WOS记录号WOS:001356599100001
出版者ELSEVIER
资助机构National Civil Aerospace Project of China
源URL[http://ir.igsnrr.ac.cn/handle/311030/210721]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Wang, Yueming
作者单位1.HuBei Univ Technol, Wuhan 430068, Hubei, Peoples R China
2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100094, Peoples R China
3.Univ Chinese Acad Sci, Hangzhou Inst Adv Study, Hangzhou 310024, Zhejiang, Peoples R China
4.Chinese Acad Sci, Shanghai Inst Tech Phys, Shanghai 200083, Peoples R China
推荐引用方式
GB/T 7714		 Zhou, Tianshu,Cen, Yi,He, Jiani,et al. K-means adaptive 2DSSA based on sparse representation model for hyperspectral target detection[J]. INFRARED PHYSICS & TECHNOLOGY,2024,143:12.
APA Zhou, Tianshu,Cen, Yi,He, Jiani,&Wang, Yueming.(2024).K-means adaptive 2DSSA based on sparse representation model for hyperspectral target detection.INFRARED PHYSICS & TECHNOLOGY,143,12.
MLA Zhou, Tianshu,et al."K-means adaptive 2DSSA based on sparse representation model for hyperspectral target detection".INFRARED PHYSICS & TECHNOLOGY 143(2024):12.

入库方式： OAI收割

来源：地理科学与资源研究所

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