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Article

Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature

1
School of Physics and Electronic Information, Yantai University, Yantai 264005, China
2
Shandong Yuweng Information Technology Co., Ltd., Yantai 264005, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(17), 5664; https://doi.org/10.3390/s24175664 (registering DOI)
Submission received: 14 July 2024 / Revised: 25 August 2024 / Accepted: 26 August 2024 / Published: 30 August 2024
(This article belongs to the Special Issue Advanced Optical Sensors Based on Machine Learning)

Abstract

In the traditional method for hyperspectral anomaly detection, spectral feature mapping is used to map hyperspectral data to a high-level feature space to make features more easily distinguishable between different features. However, the uncertainty in the mapping direction makes the mapped features ineffective in distinguishing anomalous targets from the background. To address this problem, a hyperspectral anomaly detection algorithm based on the spectral similarity variability feature (SSVF) is proposed. First, the high-dimensional similar neighborhoods are fused into similar features using AE networks, and then the SSVF are obtained using residual autoencoder. Finally, the final detection of SSVF was obtained using Reed and Xiaoli (RX) detectors. Compared with other comparison algorithms with the highest accuracy, the overall detection accuracy (AUCODP) of the SSVFRX algorithm is increased by 0.2106. The experimental results show that SSVF has great advantages in both highlighting anomalous targets and improving separability between different ground objects.
Keywords: hyperspectral anomaly detection; spectral similar variability feature; feature fusion; deep learning; residual network; autoencoder hyperspectral anomaly detection; spectral similar variability feature; feature fusion; deep learning; residual network; autoencoder

Share and Cite

MDPI and ACS Style

Li, X.; Shang, W. Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature. Sensors 2024, 24, 5664. https://doi.org/10.3390/s24175664

AMA Style

Li X, Shang W. Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature. Sensors. 2024; 24(17):5664. https://doi.org/10.3390/s24175664

Chicago/Turabian Style

Li, Xueyuan, and Wenjing Shang. 2024. "Hyperspectral Anomaly Detection Based on Spectral Similarity Variability Feature" Sensors 24, no. 17: 5664. https://doi.org/10.3390/s24175664

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