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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Veröffentlicht in:	IEEE transactions on instrumentation and measurement Jg. 73; S. 1 - 18
Hauptverfasser:	Lin, Sheng, Zhang, Min, Cheng, Xi, Shi, Lei, Gamba, Paolo, Wang, Hai
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
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