Dynamic Low-Rank and Sparse Priors Constrained Deep Autoencoders for Hyperspectral Anomaly Detection

Linear-based low-rank and sparse models (LRSM) and nonlinear-based deep autoencoder (DAE) models have been proven to be effective for the task of anomaly detection (AD) in hyperspectral images (HSIs). The linear-based LRSM is self-explainable, while it may not characterize the complex scenes well. I...

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Vydané v:	IEEE transactions on instrumentation and measurement Ročník 73; s. 1 - 18
Hlavní autori:	Lin, Sheng, Zhang, Min, Cheng, Xi, Shi, Lei, Gamba, Paolo, Wang, Hai
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Anomalies Anomaly detection (AD) Decomposition deep autoencoder (DAE) Detectors Dictionaries Feature extraction Hyperspectral imaging Image reconstruction joint optimization low-rank prior Optimization Reconstruction sparse prior Training
ISSN:	0018-9456, 1557-9662
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