Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning

Hyperspectral Anomaly Detection Using Dual-Branch Network Based on Frequency Domain Learning

Existing deep learning-based hyperspectral anomaly detection methods often overlook frequency domain features, hindering the ability to effectively distinguish between background and anomalies. Furthermore, many methods directly apply Mahalanobis distance to reconstructed hyperspectral image (HSI) f...

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Bibliographic Details
Main Authors: Xiaoyi Wang, Jun Du, Qingxuan Lv, Peng Wang, Xianchao Zhang, Jian Cheng, Henry Leung
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Frequency domain decomposition
multiscale convolution
structural similarity index (SSIM)-weighted Mahalanobis distance
transformer
Online Access:https://ieeexplore.ieee.org/document/11038935/
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