Unsupervised Anomaly Detection in Hyperspectral Imaging: Integrating Tensor Robust Principal Component Analysis With Autoencoding Adversarial Networks

Unsupervised Anomaly Detection in Hyperspectral Imaging: Integrating Tensor Robust Principal Component Analysis With Autoencoding Adversarial Networks

Hyperspectral (HS) image analysis has gained significant attention due to its ability to capture detailed spectral information across hundreds of bands, making it useful for environmental monitoring and mineral exploration applications. However, detecting anomalies in HS images, especially in comple...

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Bibliographic Details
Main Authors: Atsuya Emoto, Ryo Matsuoka
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Access
Subjects:
Hyperspectral image
anomaly detection
tensor robust principal component analysis
autoencoding adversarial networks
plug-and-play ADMM
Online Access:https://ieeexplore.ieee.org/document/10855416/
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https://ieeexplore.ieee.org/document/10855416/

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