Anomaly detection for replacement model in hyperspectral imaging

被引：6
|
作者：
Vincent, Francois [1 ]
Besson, Olivier [1 ]
Matteoli, Stefania [2 ]
机构：
[1] ISAE SUPAERO, 10 Ave Edouard Belin, F-31055 Toulouse, France
[2] CNR, IEIIT, Via Girolamo Caruso 16, Pisa, Italy
来源：
SIGNAL PROCESSING | 2021年 / 185卷
关键词：
Hyperspectral imagery; Replacement model; GLRT; Anomaly detection;
D O I：
10.1016/j.sigpro.2021.108079
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：
In this paper we consider Anomaly Detection in the hyperspectral context, and we extend the popular RX detector, initially designed under the standard additive model, to the replacement model case. Indeed, in this more realistic framework, the target, if present, is supposed to replace a part of the background. We show how to estimate this background power variation to improve the standard RX scheme. The obtained Replacement RX (RRX) is shown to be closed-form and outperforms the standard RX on a real data benchmark experiment. (c) 2021 Elsevier B.V. All rights reserved.
引用
收藏
页数：6
相关论文
共 50 条
  • [41] Greedy Ensemble Hyperspectral Anomaly Detection
    Hossain, Mazharul
    Younis, Mohammed
    Robinson, Aaron
    Wang, Lan
    Preza, Chrysanthe
    JOURNAL OF IMAGING, 2024, 10 (06)
  • [42] Hyperspectral Anomaly Detection With Guided Autoencoder
    Xiang, Pei
    Ali, Shahzad
    Jung, Soon Ki
    Zhou, Huixin
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2022, 60
  • [43] Iterative SpectralSpatial Hyperspectral Anomaly Detection
    Chang, Chein-, I
    Lin, Chien-Yu
    Chung, Pau-Choo
    Hu, Peter Fuming
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2023, 61
  • [44] HYPERSPECTRAL ANOMALY DETECTION IN URBAN SCENARIOS
    Rejas Ayuga, J. G.
    Martinez Marin, R.
    Marchamalo Sacristan, M.
    Bonatti, J.
    Ojeda, J. C.
    XXIII ISPRS CONGRESS, COMMISSION VII, 2016, 41 (B7): : 111 - 116
  • [45] Research progress on hyperspectral anomaly detection
    Qu B.
    Zheng X.
    Qian X.
    Lu X.
    National Remote Sensing Bulletin, 2024, 28 (01) : 42 - 54
  • [46] Characterization of anomaly detection in hyperspectral imagery
    Chang, Chein-I
    Hsueh, Mingkai
    Sensor Review, 2006, 26 (02) : 137 - 146
  • [47] Anomaly detection in noisy hyperspectral imagery
    Riley, RA
    Newsom, RK
    Andrews, AK
    IMAGING SPECTROMETRY X, 2004, 5546 : 159 - 170
  • [48] Anomaly detection from hyperspectral imagery
    Stein, DWJ
    Beaven, SG
    Hoff, LE
    Winter, EM
    Schaum, AP
    Stocker, AD
    IEEE SIGNAL PROCESSING MAGAZINE, 2002, 19 (01) : 58 - 69
  • [49] Collaborative Representation for Hyperspectral Anomaly Detection
    Li, Wei
    Du, Qian
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2015, 53 (03): : 1463 - 1474
  • [50] Hyperspectral anomaly detection: Beyond RX
    Schaum, A.
    Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XIII, 2007, 6565 : COVER1 - +
12345