Auto-AD: Autonomous Hyperspectral Anomaly Detection Network Based on Fully Convolutional Autoencoder

Hyperspectral anomaly detection is aimed at detecting observations that differ from their surroundings, and is an active area of research in hyperspectral image processing. Recently, autoencoders (AEs) have been applied in hyperspectral anomaly detection; however, the existing AE-based methods are c...

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Vydané v:	IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 14
Hlavní autori:	Wang, Shaoyu, Wang, Xinyu, Zhang, Liangpei, Zhong, Yanfei
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Adaptive-weighted loss Anomalies Anomaly detection autonomous detection Datasets Detection Detectors Errors Estimation Feature extraction fully convolutional autoencoder (AE) hyperspectral anomaly detection Hyperspectral imaging Image processing Image reconstruction Probability theory Training Unmanned aerial vehicles
ISSN:	0196-2892, 1558-0644
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