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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Vydané v:	IEEE geoscience and remote sensing letters Ročník 21; s. 1 - 5
Hlavní autori:	Zhang, Yongshan, Li, Yijiang, Wang, Xinxin, Jiang, Xinwei, Zhou, Yicong
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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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