Self-Supervised Masked Graph Autoencoder for Hyperspectral Anomaly Detection

Hyperspectral image anomaly detection faces the challenge of difficulty in annotating anomalous targets. Autoencoder(AE)-based methods are widely used due to their excellent image reconstruction capability. However, traditional grid-based image representation methods struggle to capture long-range d...

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Bibliographic Details
Published in:IEEE transactions on image processing Vol. 34; pp. 6714 - 6729
Main Authors: Tu, Bing, He, Baoliang, He, Yan, Zhou, Tao, Liu, Bo, Li, Jun, Plaza, Antonio
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2025
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Anomalies
Anomaly detection
autoencoder
Autoencoders
Decoding
Detectors
Feature extraction
Gaussian distribution
graph attention network
Hyperspectral imaging
Image reconstruction
Kernel
Laplace equations
re-masking strategy
Regularization
Representations
twice loss
ISSN:1057-7149, 1941-0042, 1941-0042
Online Access:Get full text
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Summary:Hyperspectral image anomaly detection faces the challenge of difficulty in annotating anomalous targets. Autoencoder(AE)-based methods are widely used due to their excellent image reconstruction capability. However, traditional grid-based image representation methods struggle to capture long-range dependencies and model non-Euclidean structures. To address these issues, this paper proposes a self-supervised Masked Graph AutoEncoder (MGAE) for hyperspectral anomaly detection. MGAE utilizes a Graph Attention Network (GAT) autoencoder to reconstruct the background of hyperspectral images and identifies anomalies by comparing the reconstructed features with the original features. Specifically, we constructs a topological graph structure of the hyperspectral image, which is then input into the GAT autoencoder for reconstruction, leveraging the multi-head attention mechanism to learn spatial and spectral features. To prevent the decoder from learning trivial solutions, we introduce a re-masking strategy that randomly masks both the input features and hidden representations during training, forcing the model to learn and reconstruct features under limited information, thereby improving detection performance. Additionally, the proposed loss function with graph Laplacian regularization (Twice Loss) minimizes variations in feature representations, leading to more consistent background reconstruction. Experimental results on several real-world hyperspectral datasets demonstrate that MGAE outperforms existing methods.
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ISSN:1057-7149
1941-0042
1941-0042
DOI:10.1109/TIP.2025.3620091