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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Veröffentlicht in:IEEE geoscience and remote sensing letters Jg. 21; S. 1 - 5
Hauptverfasser: Zhang, Yongshan, Li, Yijiang, Wang, Xinxin, Jiang, Xinwei, Zhou, Yicong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
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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Zusammenfassung: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.
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ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2024.3416454