Spectral-Spatial Deep Support Vector Data Description for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) aims to distinguish anomalies from background-by-background modeling. Deep learning has been applied to HAD and achieves promising detection results. However, there exist several issues that need to be addressed: 1) unrealistic Gaussian assumption on the latent...

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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 60; s. 1 - 16
Hlavní autoři: Li, Kun, Ling, Qiang, Qin, Yao, Wang, Yingqian, Cai, Yaoming, Lin, Zaiping, An, Wei
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Anomalies
Anomaly detection
Clustering
Complementarity
Deep learning
deep support vector data description (DSVDD)
Detection
Exploitation
Feature extraction
Gaussian distribution
hyperspectral imagery (HSI)
Hyperspectral imaging
Hyperspheres
Kernel
Machine learning
one-class classification
Redundancy
Representation learning
Spectra
Training
ISSN:0196-2892, 1558-0644
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Shrnutí:Hyperspectral anomaly detection (HAD) aims to distinguish anomalies from background-by-background modeling. Deep learning has been applied to HAD and achieves promising detection results. However, there exist several issues that need to be addressed: 1) unrealistic Gaussian assumption on the latent representations may limit its application; 2) deep features are not well-suited to anomaly detection due to the separation between feature learning and anomaly detection; 3) lack of adequate exploitation of spectral-spatial features; 4) negative effect caused by spectral band redundancy. In this article, we propose an end-to-end trainable deep one-class classification network for HAD. Specifically, a minimal enclosing hypersphere is trained to involve the deep features of background samples. These background samples are selected by a density clustering-based method. In this way, feature learning and anomaly detection are incorporated into a unified framework. Meanwhile, there is no explicit Gaussian assumption on the background features. Moreover, due to the complementarity of spectral and spatial features, a novel feature fusion strategy is proposed to fuse spectral and spatial features extracted by a two-stream deep convolutional autoencoder network. Finally, a band attention module is used to automatically learn small weights for redundant bands and thus reduce the negative effect caused by redundant bands. Experimental results on five public datasets demonstrate the superiority of the proposed method compared to several state-of-the-art HAD methods in the detection performance.
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ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2022.3144192