A real-time anomaly detection algorithm for hyperspectral imagery based on causal processing

被引：2

作者：

Zhao Chun-Hui ^{[1
]}

Wang Yu-Lei ^{[1
,2
]}

Li Xiao-Hui ^{[1
,3
]}

机构：

[1] Harbin Engn Univ, Informat & Commun Engn Coll, Harbin 150001, Peoples R China

[2] Univ Maryland Baltimore Cty, Dept Comp Sci & Elect Engn, Remote Sensing Signal & Image Proc Lab, Baltimore, MD 21250 USA

[3] Univ Strathclyde, Dept Elect & Elect Engn, Ctr Excellence Signal & Image Proc, Glasgow G1 1XW, Lanark, Scotland

来源：

JOURNAL OF INFRARED AND MILLIMETER WAVES | 2015年 / 34卷 / 01期

基金：

中国国家自然科学基金; 中国博士后科学基金;

关键词：

hyperspectral anomaly detection; real-time algorithm; Woodbury's identity;

D O I：

10.3724/SP.J.1010.2015.00114

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

Anomaly detection is one of the most important applications in hyperspectral imagery. Real-time processing is the main issue we are facing due to the large data set. Real time causal processing algorithms were developed to perform anomaly detection. It is an innovational kalman filtering based processing by using Woodbury's identity to update information which provides the pixel currently being processed without re-processing previous pixels. Experimental results demonstrated the proposed algorithm significantly improves processing efficiency in comparison with conventional anomaly detection without real time causal processing.

引用

页码：114 / 121

页数：8

共 9 条

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