Kernel Minimum Noise Fraction Transformation-Based Background Separation Model for Hyperspectral Anomaly Detection

Kernel Minimum Noise Fraction Transformation-Based Background Separation Model for Hyperspectral Anomaly Detection

A significant challenge in methods for anomaly detection (AD) in hyperspectral images (HSIs) is determining how to construct an efficient representation for anomalies and background information. Considering the high-order structures of HSIs and the estimation of anomalies and background information...

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Bibliographic Details
Main Authors: Tianru Xue, Jianxin Jia, Hui Xie, Changxing Zhang, Xuan Deng, Yueming Wang
Format: Article
Language:English
Published: MDPI AG 2022-10-01
Series:Remote Sensing
Subjects:
anomaly detection (AD)
background separation model (BSM)
feature extraction
hyperspectral images (HSIs)
normalized Euclidean distance (NED)
Online Access:https://www.mdpi.com/2072-4292/14/20/5157

Internet

https://www.mdpi.com/2072-4292/14/20/5157

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