Spectral-Spatial Anomaly Detection via Collaborative Representation Constraint Stacked Autoencoders for Hyperspectral Images

Nowadays, due to the ability of extracting deep features, the deep learning-based anomaly detection (AD) methods for hyperspectral images (HSIs) have been widely studied. However, all these AD methods treat the tasks of feature extraction and AD separately. Besides, most of them also do not make use...

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Bibliographic Details
Published in:	IEEE geoscience and remote sensing letters Vol. 19; pp. 1 - 5
Main Authors:	Zhao, Chunhui, Li, Chuang, Feng, Shou, Li, Wei
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	Anomalies Anomaly detection Collaboration Collaborative representation Deep learning Detection Detectors Feature extraction hyperspectral anomaly detection (AD) hyperspectral images (HSIs) Hyperspectral imaging Machine learning Mathematical model Methods Representations Spatial data Spectra spectral–spatial information stacked autoencoders (SAEs) Task analysis
ISSN:	1545-598X, 1558-0571
Online Access:	Get full text
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