Hyperspectral Anomaly Detection Using Deep Learning: A Review

Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots in the field of remote sensing. Because HSI’s features of integrating image and spectrum provide a considerable data basis for abnormal object detection, HSI-AD has a huge application potential in HSI analysis. It...

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Veröffentlicht in:Remote sensing (Basel, Switzerland) Jg. 14; H. 9; S. 1973
Hauptverfasser: Hu, Xing, Xie, Chun, Fan, Zhe, Duan, Qianqian, Zhang, Dawei, Jiang, Linhua, Wei, Xian, Hong, Danfeng, Li, Guoqiang, Zeng, Xinhua, Chen, Wenming, Wu, Dongfang, Chanussot, Jocelyn
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Basel MDPI AG 01.05.2022
Schlagworte:
Anomalies
Datasets
Deep learning
Feature extraction
hyperspectral image-anomaly detection
Hyperspectral imaging
Machine learning
Object recognition
Performance assessment
Remote sensing
Reviews
Sensors
Spectrum analysis
Los Angeles California
United States > US
Texas
ISSN:2072-4292, 2072-4292
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Zusammenfassung:Hyperspectral image-anomaly detection (HSI-AD) has become one of the research hotspots in the field of remote sensing. Because HSI’s features of integrating image and spectrum provide a considerable data basis for abnormal object detection, HSI-AD has a huge application potential in HSI analysis. It is difficult to effectively extract a large number of nonlinear features contained in HSI data using traditional machine learning methods, and deep learning has incomparable advantages in the extraction of nonlinear features. Therefore, deep learning has been widely used in HSI-AD and has shown excellent performance. This review systematically summarizes the related reference of HSI-AD based on deep learning and classifies the corresponding methods into performance comparisons. Specifically, we first introduce the characteristics of HSI-AD and the challenges faced by traditional methods and introduce the advantages of deep learning in dealing with these problems. Then, we systematically review and classify the corresponding methods of HSI-AD. Finally, the performance of the HSI-AD method based on deep learning is compared on several mainstream data sets, and the existing challenges are summarized. The main purpose of this article is to give a more comprehensive overview of the HSI-AD method to provide a reference for future research work.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2072-4292
2072-4292
DOI:10.3390/rs14091973