Anomaly detection from hyperspectral imagery

被引：549
|
作者：
Stein, DWJ [1 ]
Beaven, SG
Hoff, LE
Winter, EM
Schaum, AP
Stocker, AD
机构：
[1] SPAWAR Syst Ctr, San Diego, CA USA
[2] Naval Res Lab, Washington, DC USA
[3] Space Comp Corp, Los Angeles, CA USA
来源：
IEEE SIGNAL PROCESSING MAGAZINE | 2002年 / 19卷 / 01期
关键词：
D O I：
10.1109/79.974730
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
Anomaly detectors for hyperspectral data were developed based on fundamental detection theoretic principles, including the generalized likelihood ratio test (GLRT) and approximations thereof. As such, the underlying theory for the application of anomaly detection to systems with inherently high dimensionality was outlined. It was shown that the performance improves with SNR depends on the characteristics of the targets, clutter, environment, and sensor.
引用
收藏
页码：58 / 69
页数：12
相关论文
共 50 条
  • [31] A support vector method for anomaly detection in hyperspectral imagery
    Banerjee, Amit
    Burlina, Philippe
    Diehl, Chris
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2006, 44 (08): : 2282 - 2291
  • [32] ANOMALY DETECTION BASED ON QUADRATIC MODELING OF HYPERSPECTRAL IMAGERY
    Zhong, Shengwei
    Zhang, Ye
    2016 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2016, : 5864 - 5867
  • [33] The Impacts of Decomposition Levels in Wavelet Transform on Anomaly Detection from Hyperspectral Imagery
    Yoo, Hee Young
    Park, No-Wook
    KOREAN JOURNAL OF REMOTE SENSING, 2012, 28 (06) : 623 - 632
  • [34] Parallel and Distributed Computing for Anomaly Detection From Hyperspectral Remote Sensing Imagery
    Du, Qian
    Tang, Bo
    Xie, Weiying
    Li, Wei
    PROCEEDINGS OF THE IEEE, 2021, 109 (08) : 1306 - 1319
  • [35] Kernel ICA Feature Extraction for Anomaly Detection in Hyperspectral Imagery
    Zhao Chunhui
    Wang Yulei
    Mei Feng
    CHINESE JOURNAL OF ELECTRONICS, 2012, 21 (02): : 265 - 269
  • [36] Spectral Adversarial Feature Learning for Anomaly Detection in Hyperspectral Imagery
    Xie, Weiying
    Liu, Baozhu
    Li, Yunsong
    Lei, Jie
    Chang, Chein-, I
    He, Gang
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (04): : 2352 - 2365
  • [37] Adaptive anomaly detection using subspace separation for hyperspectral imagery
    Kwon, HS
    Der, SZ
    Nasrabadi, NM
    OPTICAL ENGINEERING, 2003, 42 (11) : 3342 - 3351
  • [38] Anomaly detection based on extended mathematical morphology for hyperspectral imagery
    Li, Na
    Zhao, Huijie
    Jia, Guorui
    Dong, Chao
    Guangxue Xuebao/Acta Optica Sinica, 2008, 28 (08): : 1480 - 1484
  • [39] Dual window-based anomaly detection for hyperspectral imagery
    Kwon, H
    Der, SZ
    Nasrabadi, NM
    AUTOMATIC TARGET RECOGNITION XIII, 2003, 5094 : 148 - 158
  • [40] Anomaly detection for hyperspectral imagery based on vertex component analysis
    Aerospace TT and C System Laboratory, School of Mechatronic Engineering, Beijing Institute of Technology, Beijing 100081, China
    不详
    Yuhang Xuebao, 2007, 5 (1262-1265):
12345