Anomaly and target detection by means of non-parametric density estimation

被引：2
|
作者：
Tidhar, G. A. [1 ]
Rotman, S. R. [1 ]
机构：
[1] Ben Gurion Univ Negev, Dept Elect & Comp Engn, IL-84105 Beer Sheva, Israel
来源：
ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XVIII | 2012年 / 8390卷
关键词：
Hyperspectral Imaging; Target detection; non parametric; DISCRETE PROBABILITY-DISTRIBUTIONS; HYPERSPECTRAL IMAGERY; COVARIANCE-SELECTION; DEPENDENCE TREES; CLASSIFICATION; PATTERN;
D O I：
10.1117/12.919638
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：
We describe a novel completely non parametric high-dimension joint density estimation algorithm suited for anomaly and target detection using hyperspectral imaging. The new algorithm is compared against linear matched filter detection schemes with different available sample sizes, background statistics (MVN, GMM and non Gaussian). The new algorithm is shown to be superior in important cases.
引用
收藏
页数：12
相关论文
共 50 条
  • [1] Bivariate non-Parametric Anomaly Detection
    Callegari, Christian
    Giordano, Stefano
    Pagano, Michele
    [J]. 2014 IEEE INTERNATIONAL CONFERENCE ON HIGH PERFORMANCE COMPUTING AND COMMUNICATIONS, 2014 IEEE 6TH INTL SYMP ON CYBERSPACE SAFETY AND SECURITY, 2014 IEEE 11TH INTL CONF ON EMBEDDED SOFTWARE AND SYST (HPCC,CSS,ICESS), 2014, : 810 - 813
  • [2] Target Detection in Inhomogeneous Non-Gaussian Hyperspectral Data, Based On Non-Parametric Density Estimation
    Tidhar, G. A.
    Rotman, S. R.
    [J]. ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XIX, 2013, 8743
  • [3] TARGET DETECTION IN INHOMOGENOUS NON-GAUSSIAN HYPERSPECTRAL DATA, BASED ON NON-PARAMETRIC DENSITY ESTIMATION
    Tidhar, G. A.
    Rotman, S. R.
    [J]. 2013 5TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS), 2013,
  • [4] Detection of Periodic Problems using Non-Parametric Density Estimation
    Sarkan, Mehmet Onur
    Cataltepe, Zehra
    [J]. 2013 21ST SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2013,
  • [5] Density estimation with non-parametric methods
    Fadda, D
    Slezak, E
    Bijaoui, A
    [J]. ASTRONOMY & ASTROPHYSICS SUPPLEMENT SERIES, 1998, 127 (02): : 335 - 352
  • [6] Anomaly detection using non-parametric information
    Varma, Anil
    Bonissone, Piero
    Yan, Weizhong
    Eklund, Neil
    Goebel, Kai
    Iyer, Naresh
    Bonissone, Stefano
    [J]. PROCEEDINGS OF THE ASME TURBO EXPO 2007, VOL 1, 2007, : 813 - 821
  • [7] Detection threshold for non-parametric estimation
    Abdourrahmane M. Atto
    Dominique Pastor
    Gregoire Mercier
    [J]. Signal, Image and Video Processing, 2008, 2 : 207 - 223
  • [8] Detection threshold for non-parametric estimation
    Atto, Abdourrahmane M.
    Pastor, Dominique
    Mercier, Gregoire
    [J]. SIGNAL IMAGE AND VIDEO PROCESSING, 2008, 2 (03) : 207 - 223
  • [9] A fast non-parametric density estimation algorithm
    Egecioglu, O
    Srinivasan, A
    [J]. COMMUNICATIONS IN NUMERICAL METHODS IN ENGINEERING, 1997, 13 (10): : 755 - 763
  • [10] A geometric approach to non-parametric density estimation
    Browne, Matthew
    [J]. PATTERN RECOGNITION, 2007, 40 (01) : 134 - 140
12345