USING SUPPORT VECTOR MACHINES FOR ANOMALOUS CHANGE DETECTION

被引：4

作者：

Steinwart, Ingo ^{[1
]}

Theiler, James ^{[1
]}

Llamocca, Daniel ^{[1
]}

机构：

[1] Los Alamos Natl Lab, Los Alamos, NM 87545 USA

来源：

2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM | 2010年

关键词：

anomaly; change detection; machine learning; classification; support vector machine; graphical processing unit; HYPERSPECTRAL CHANGE DETECTION;

D O I：

10.1109/IGARSS.2010.5651836

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

We cast anomalous change detection as a binary classification problem, and use a support vector machine (SVM) to build a detector that does not depend on assumptions about the underlying data distribution. To speed up the computation, our SVM is implemented, in part, on a graphical processing unit. Results on real and simulated anomalous changes are used to compare performance to algorithms which effectively assume a Gaussian distribution.

引用

页码：3732 / 3735

页数：4

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