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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Vydané v:IEEE geoscience and remote sensing letters Ročník 19; s. 1 - 5
Hlavní autori: Zhao, Chunhui, Li, Chuang, Feng, Shou, Li, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
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
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Popis
Shrnutí: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 of abundant spatial information of HSIs. Thus, a spectral-spatial hyperspectral AD method via collaborative representation constraint stacked autoencoders (SSCRSAE) is proposed. First, the collaborative representation constraint is imposed on the stacked autoencoders to extract deep nonlinear features that are more suitable for the collaborative representation-based detector (CRD). Then, CRD is used to for obtaining the preliminary detection result, which is more convenient for real HSIs because of no need for assuming the distribution of the background. Finally, aiming at further improving the SSCRSAE detector's performance, a novel spectral-spatial AD procedure is designed for calculating the final detection result by considering the spatial information of an HSI. Experimental results express that the proposed SSCRSAE exceeds eight state-of-the-art anomaly detectors used for comparison.
Bibliografia:ObjectType-Article-1
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content type line 14
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2021.3050308