Based on the Clustering of the Background for Hyperspectral Imaging Anomaly Detection

被引：0
|
作者：
Li Xiaohui [1 ]
Zhao Chunhui [1 ]
机构：
[1] Harbin Engn Univ, Coll Informat & Commun Engn, Harbin, Peoples R China
来源：
2011 INTERNATIONAL CONFERENCE ON ELECTRONICS, COMMUNICATIONS AND CONTROL (ICECC) | 2011年
关键词：
hyperspectral image; anomaly target detection; EM algorithm; RX algorithm; smooth background;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
RX algorithm is the most classical algorithm in hyperspectral image anomaly detection algorithm, but the detection effect down significantly in a complicated and nonhomogeneous background. This paper use EM algorithm to smooth background by clustering the adjacent area of the pixel under test (PUT); in the process of detection, using the average of clustering replace the original background, in order to reduce the influence of the background complexity on the detection algorithm. With AVIRIS hyperspectral data, the simulation experiment has good detection effect.
引用
收藏
页码：1345 / 1348
页数：4
相关论文
共 50 条
  • [41] Hyperspectral anomaly detection based on spectral-spatial background joint sparse representation
    Zhang, Lili
    Zhao, Chunhui
    EUROPEAN JOURNAL OF REMOTE SENSING, 2017, 50 (01) : 362 - 376
  • [42] Hyperspectral Anomaly Detection by the Use of Background Joint Sparse Representation
    Li, Jiayi
    Zhang, Hongyan
    Zhang, Liangpei
    Ma, Li
    IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, 2015, 8 (06) : 2523 - 2533
  • [43] Autoencoder and Adversarial-Learning-Based Semisupervised Background Estimation for Hyperspectral Anomaly Detection
    Xie, Weiying
    Liu, Baozhu
    Li, Yunsong
    Lei, Jie
    Du, Qian
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2020, 58 (08): : 5416 - 5427
  • [44] A Feature-clustering-based Subspace Ensemble Method For Anomaly Detection In Hyperspectral Imagety
    Liu, Zhenlin
    Gu, Yanfeng
    Wang, Chen
    Han, Jinglong
    Zhang, Ye
    2011 6TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA), 2011, : 2274 - 2277
  • [45] UADNet: A Joint Unmixing and Anomaly Detection Network Based on Deep Clustering for Hyperspectral Image
    Liu, Wendi
    Ma, Yong
    Wang, Xiaozhu
    Huang, Jun
    Chen, Qihai
    Li, Hao
    Mei, Xiaoguang
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2024, 62 (1-19): : 1 - 19
  • [46] Consensus Anomaly Detection Using Clustering Methods in Hyperspectral imagery
    Amiel, Yoav
    Frajman, Adar
    Rotman, Stanley R.
    IMAGING SPECTROMETRY XXIV: APPLICATIONS, SENSORS, AND PROCESSING, 2020, 11504
  • [47] Characterization of Background-Anomaly Separability With Generative Adversarial Network for Hyperspectral Anomaly Detection
    Zhong, Jiaping
    Xie, Weiying
    Li, Yunsong
    Lei, Jie
    Du, Qian
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2021, 59 (07): : 6017 - 6028
  • [48] Anomaly detection using the hyperspectral polarimetric imaging testbed
    Cavanaugh, David B.
    Castle, Kenneth R.
    Davenport, Wayne
    ALGORITHMS AND TECHNOLOGIES FOR MULTISPECTRAL, HYPERSPECTRAL, AND ULTRASPECTRAL IMAGERY XII PTS 1 AND 2, 2006, 6233
  • [49] Semi-Supervised Hyperspectral Anomaly Detection Based on Spatial-Spectral Background Reconstruction
    Li Luyao
    Li Zhongwei
    Wang Leiquan
    Li Juan
    Shi Shunxiao
    LASER & OPTOELECTRONICS PROGRESS, 2023, 60 (20)
  • [50] Hyperspectral anomaly detection based on local contrast estimation and sub-block background estimation
    Zhang, Jiajia
    Xu, Xingchen
    Yan, Weiming
    Li, Huan
    Xiang, Pei
    Song, Jiangluqi
    Zhao, Dong
    Tan, Wei
    INFRARED PHYSICS & TECHNOLOGY, 2023, 135
12345