Stacked Graph Fusion Denoising Autoencoder for Hyperspectral Anomaly Detection

Anomaly detection for hyperspectral images (HSIs) is a challenging problem to distinguish a few anomalous pixels from a majority of background pixels. Most existing methods cannot simultaneously explore both structural and spatial information from global and local perspectives. In this letter, we pr...

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Published in:	IEEE geoscience and remote sensing letters Vol. 21; pp. 1 - 5
Main Authors:	Zhang, Yongshan, Li, Yijiang, Wang, Xinxin, Jiang, Xinwei, Zhou, Yicong
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Anomalies Anomaly detection denoising autoencoder Detectors Geoscience and remote sensing graph neural network Graphs hyperspectral imagery Hyperspectral imaging Image edge detection Image reconstruction Multisensor fusion Noise reduction Pixels Spatial data Training
ISSN:	1545-598X, 1558-0571
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Abstract	Anomaly detection for hyperspectral images (HSIs) is a challenging problem to distinguish a few anomalous pixels from a majority of background pixels. Most existing methods cannot simultaneously explore both structural and spatial information from global and local perspectives. In this letter, we propose a stacked graph fusion denoising autoencoder (SGFDAE) for hyperspectral anomaly detection. Specifically, the global and local graphs are constructed from an HSI to explore potential structural and spatial information. With the designed graph fusion strategy, an advanced graph denoising autoencoder with deep architecture is developed in a hierarchical manner. To achieve better reconstruction and detection, a greedy layerwise unsupervised pretraining strategy is presented for network training. Experiments show that SGFDAE achieves 97.17%, 98.43%, and 98.90% detection accuracies by averaging the results of the datasets from three different scenes and outperforms the state-of-the-art methods.
AbstractList	Anomaly detection for hyperspectral images (HSIs) is a challenging problem to distinguish a few anomalous pixels from a majority of background pixels. Most existing methods cannot simultaneously explore both structural and spatial information from global and local perspectives. In this letter, we propose a stacked graph fusion denoising autoencoder (SGFDAE) for hyperspectral anomaly detection. Specifically, the global and local graphs are constructed from an HSI to explore potential structural and spatial information. With the designed graph fusion strategy, an advanced graph denoising autoencoder with deep architecture is developed in a hierarchical manner. To achieve better reconstruction and detection, a greedy layerwise unsupervised pretraining strategy is presented for network training. Experiments show that SGFDAE achieves 97.17%, 98.43%, and 98.90% detection accuracies by averaging the results of the datasets from three different scenes and outperforms the state-of-the-art methods.
Author	Wang, Xinxin Li, Yijiang Jiang, Xinwei Zhou, Yicong Zhang, Yongshan
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Snippet	Anomaly detection for hyperspectral images (HSIs) is a challenging problem to distinguish a few anomalous pixels from a majority of background pixels. Most...
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SubjectTerms	Anomalies Anomaly detection denoising autoencoder Detectors Geoscience and remote sensing graph neural network Graphs hyperspectral imagery Hyperspectral imaging Image edge detection Image reconstruction Multisensor fusion Noise reduction Pixels Spatial data Training
Title	Stacked Graph Fusion Denoising Autoencoder for Hyperspectral Anomaly Detection
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