Anomaly Detection with Bayesian Gauss Background Model in Hyperspectral Images

被引：0

作者：

Sahin, Yunus Emre ^{[1
]}

Arisoy, Sertac ^{[2
]}

Kayabol, Koray ^{[2
]}

机构：

[1] TUBITAK, BILGEM, Kocaeli, Turkey

[2] Gebze Tekn Univ, Elekt Muhendisligi Bolumu, Kocaeli, Turkey

来源：

2018 26TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU) | 2018年

关键词：

Hyperspecral Image; Bayesian Gaussian Background Model; Anomaly Detection; Reed Xiaoli; RX; ALGORITHM;

D O I：

暂无

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 propose Bayesian Gaussian Background Model (BGBM) for anomaly detection problem on hyperspectral images. BGBM is used in a local window for learning the background. BGBM gives better results than existing methods on real hyperspectral images in the estimation of covariance matrix in case of limited number of samples.

引用

页数：4

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