Hyperspectral Anomaly Detection Based on Regularized Background Abundance Tensor Decomposition

Hyperspectral Anomaly Detection Based on Regularized Background Abundance Tensor Decomposition

The low spatial resolution of hyperspectral images means that existing mixed pixels rely heavily on spectral information, making it difficult to differentiate between the target of interest and the background. The endmember extraction method is powerful in enhancing spatial structure information for...

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Bibliographic Details
Main Authors: Wenting Shang, Mohamad Jouni, Zebin Wu, Yang Xu, Mauro Dalla Mura, Zhihui Wei
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Remote Sensing
Subjects:
anomaly detection
tensor decomposition
hyperspectral images
low-rank representation
linear mixing model
background abundance tensor
Online Access:https://www.mdpi.com/2072-4292/15/6/1679

Internet

https://www.mdpi.com/2072-4292/15/6/1679

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