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

页数：6

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