Anomaly Detection in Hyperspectral Image Using 3D-Convolutional Variational Autoencoder

Anomaly detection (AD) has become a hot topic in hyperspectral image (HSI) analysis. Anomalies are samples that are significantly different from the surrounding background in space or spectrum. However, the rich spectral-spatial features in HSI are not fully discovered by most traditional AD methods...

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Bibliographic Details
Published in:IEEE International Geoscience and Remote Sensing Symposium proceedings pp. 2512 - 2515
Main Authors: Zhang, Jingfa, Xu, Yang, Zhan, Tianming, Wu, Zebin, Wei, Zhihui
Format: Conference Proceeding
Language:English
Published: IEEE 11.07.2021
Subjects:
Anomaly detection
Anomaly detection (AD)
convolutional Variational Autoencoder (CVAE)
Decoding
Detectors
Feature extraction
Geoscience and remote sensing
hyperspectral image (HSI)
Image reconstruction
RX detector
Training
ISSN:2153-7003
Online Access:Get full text
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Summary:Anomaly detection (AD) has become a hot topic in hyperspectral image (HSI) analysis. Anomalies are samples that are significantly different from the surrounding background in space or spectrum. However, the rich spectral-spatial features in HSI are not fully discovered by most traditional AD methods. In this paper, a 3D-convolutional Variational Au-toencoder (3D-CVAE) based AD method is proposed to make full use of the spectral-spatial information. The spectral-spatial features are extracted by the 3D-CVAE encoder and the background is reconstructed using these features through 3D-CVAE decoder. The residual between the original input and the reconstructed background contains the anomalies which can be easily detected by the Reed-Xiaoli(RX) detector in the residual. Experimental results on two HSI datasets demonstrate the advantage of the proposed method.
ISSN:2153-7003
DOI:10.1109/IGARSS47720.2021.9554184