Spatial–Spectral Joint Hyperspectral Anomaly Detection Based on a Two-Branch 3D Convolutional Autoencoder and Spatial Filtering

Hyperspectral anomaly detection (HAD) is an important application of hyperspectral images (HSI) that can distinguish anomalies from background in an unsupervised manner. As a common unsupervised network in deep learning, autoencoders (AE) have been widely used in HAD and can highlight anomalies by r...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 15; no. 10; p. 2542
Main Authors: Lv, Shuai, Zhao, Siwei, Li, Dandan, Pang, Boyu, Lian, Xiaoying, Liu, Yinnian
Format: Journal Article
Language:English
Published: Basel MDPI AG 12.05.2023
Subjects:
3D convolutional autoencoder
Algorithms
Anomalies
anomaly detection
Artificial intelligence
Collaboration
Comparative analysis
data collection
Datasets
Decomposition
Deep learning
hyperspectral image
Hyperspectral imaging
Image processing
Machine learning
Methods
Morphological filters
Multispectral photography
Neural networks
Normal distribution
Remote sensing
satellites
Sparsity
Spatial filtering
spatial–spectral joint information
Technology application
variance
China
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:Hyperspectral anomaly detection (HAD) is an important application of hyperspectral images (HSI) that can distinguish anomalies from background in an unsupervised manner. As a common unsupervised network in deep learning, autoencoders (AE) have been widely used in HAD and can highlight anomalies by reconstructing the background. This study proposed a novel spatial–spectral joint HAD method based on a two-branch 3D convolutional autoencoder and spatial filtering. We used the two-branch 3D convolutional autoencoder to fully extract the spatial–spectral joint features and spectral interband features of HSI. In addition, we used a morphological filter and a total variance curvature filter for spatial detection. Currently, most of the datasets used to validate the performance of HAD methods are airborne HSI, and there are few available satellite-borne HSI. For this reason, we constructed a dataset of satellite-borne HSI based on the GF-5 satellite for experimental validation of our anomaly detection method. The experimental results for the airborne and satellite-borne HSI demonstrated the superior performance of the proposed method compared with six state-of-the-art methods. The area under the curve (AUC) values of our proposed method on different HSI reached above 0.9, which is higher than those of the other methods.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs15102542