Hyperspectral Anomaly Detection via Graphical Connected Point Estimation and Multiple Support Vector Machines

Hyperspectral Anomaly Detection via Graphical Connected Point Estimation and Multiple Support Vector Machines

Most hyperspectral anomaly detection algorithms are based on various hypothetical models justified by different methods. The closer to the real-world scene distribution a hypothetical model is, the better detection performance usually results, albeit at the expense of increased model complexity. The...

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Bibliographic Details
Main Authors: Shangzhen Song, Hanlin Qin, Yixin Yang, Zhe Zhang, Huixin Zhou
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Hyperspectral image
anomaly detection
graphical data description
clustering
machine learning
support vector machine
Online Access:https://ieeexplore.ieee.org/document/9096329/

Internet

https://ieeexplore.ieee.org/document/9096329/

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