Self-Supervised Marine Noise Learning with Sparse Autoencoder Network for Generative Target Magnetic Anomaly Detection

As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challe...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Vol. 16; no. 17; p. 3263
Main Authors: Wang, Shigang, Zhang, Xiangyuan, Zhao, Yifan, Yu, Haozi, Li, Bin
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.09.2024
Subjects:
data collection
Deep learning
Design
Detectors
Edge detection
Effectiveness
Ferromagnetism
Geomagnetic field
Learning
Magnetic anomalies
magnetic anomaly detection (MAD)
Magnetic fields
marine noise modeling
Methods
monitoring
Neural networks
Performance indices
Sea trials
self-supervised learning (SSL)
Sensors
sparse denoising autoencoder (SDAE)
Statistical models
Surveillance
Target acquisition
Target detection
Task complexity
Wavelet transforms
ISSN:2072-4292, 2072-4292
Online Access:Get full text
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Summary:As an effective physical field feature to perceive ferromagnetic targets, magnetic anomaly is widely used in covert marine surveillance tasks. However, its practical usability is affected by the complex marine magnetic noise interference, making robust magnetic anomaly detection (MAD) quite a challenging task. Recently, learning-based detectors have been widely studied for the discrimination of magnetic anomaly signal and achieve superior performance than traditional rule-based detectors. Nevertheless, learning-based detectors require abundant data for model parameter training, which are difficult to access in practical marine applications. In practice, target magnetic anomaly data are usually expensive to acquire, while rich marine magnetic noise data are readily available. Thus, there is an urgent need to develop effective models to learn discriminative features from the abundant marine magnetic noise data for newly appearing target anomaly detection. Motivated by this, in this paper we formulate MAD as a single-edge detection problem and develop a self-supervised marine noise learning approach for target anomaly classification. Specifically, a sparse autoencoder network is designed to model the marine noise and restore basis geomagnetic field from the collected noisy magnetic data. Subsequently, reconstruction error of the network is used as a statistical decision criterion to discriminate target magnetic anomaly from cluttered noise. Finally, we verify the effectiveness of the proposed approach on real sea trial data and compare it with seven state-of-the-art MAD methods on four numerical indexes. Experimental results indicate that it achieves a detection accuracy of 93.61% and has a running time of 21.06 s on the test dataset, showing superior MAD performance over its counterparts.
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ISSN:2072-4292
2072-4292
DOI:10.3390/rs16173263