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Self-Supervised Masked Graph Autoencoder for Hyperspectral Anomaly Detection

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Proposed masked graph autoencoder improves anomaly detection in hyperspectral images, utilizing graph attention networks.

MGAE significantly improves anomaly detection performance in hyperspectral images, overcoming traditional limitations.
The model demonstrated superior background reconstruction capabilities compared to existing autoencoder methods.
Through a self-supervised approach and re-masking strategy, MGAE effectively learns feature representations.
Experimental results on real-world datasets confirm the effectiveness of the proposed graph Laplacian regularization.

Tu et al. (2025) studied this question.

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View Full Paper

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Authors

Proposed masked graph autoencoder improves anomaly detection in hyperspectral images, utilizing graph attention networks.

MGAE significantly improves anomaly detection performance in hyperspectral images, overcoming traditional limitations.
The model demonstrated superior background reconstruction capabilities compared to existing autoencoder methods.
Through a self-supervised approach and re-masking strategy, MGAE effectively learns feature representations.
Experimental results on real-world datasets confirm the effectiveness of the proposed graph Laplacian regularization.

Tu et al. (2025) studied this question.

View Full Paper

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Bookmark