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Multiscale Memory Autoencoder and Spatial Filtering for Hyperspectral Anomaly Detection
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AI
DOI：10.1109/LGRS.2025.3528498.png)
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Abstract
En 中文
The hyperspectral anomaly detection (HAD) aims to identify potential anomalies from complex backgrounds. Most reconstruction-based autoencoders equally treat background pixels and anomalies or ignore potential spatial information. In this letter, we propose an HAD method based on multiscale memory autoencoder and spatial filtering, abbreviated as SFM2AE. Specifically, by introducing memory modules into different hidden layers of the autoencoder, multiscale reconstruction of background and anomaly pixels is achieved in the spectral domain. In addition, morphological filtering in the spatial domain is used to extract spatial structural information from anomalies. Joint spatial-spectral anomaly detection is achieved by combining multiscale memory autoencoder and spatial filtering. Experiments demonstrate superior detection performance of the proposed method over the state-of-the-art methods.
Keywords:
Autoencoders
Image reconstruction
Anomaly detection
Memory modules
Filtering
Feature extraction
Decoding
Training
Statistical analysis
Spectral analysis
Autoencoder
hyperspectral anomaly detection (HAD)
memory module
spatial filtering
Journal
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Citations:
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