Brain-Inspired Fast Saliency-Based Filtering Algorithm for Ship Detection in High-Resolution SAR Images

In this article, we aim to improve the performance of synthetic aperture radar (SAR) ship detection under complex conditions. The complex backgrounds are commonly encountered for high-resolution (HR) SAR ship detection data set, and they greatly influence the detection performance of ships. In recen...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 60; S. 1 - 9
Hauptverfasser: Zhang, Panpan, Luo, Haibo, Ju, Moran, He, Miao, Chang, Zheng, Hui, Bin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 2022
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Artificial neural networks
Brain
Brain-inspired
deep neural networks (DNNs)
Detection
Detectors
Fast Saliency-based Filtering algorithm (FSF)
Feature extraction
filtering mechanisms
Filtration
Focusing
High resolution
Image resolution
Information processing
Marine vehicles
Neural networks
Noise sensitivity
Performance enhancement
priority map
Radar detection
Radar polarimetry
Resolution
Salience
saliency map
SAR (radar)
Synthetic aperture radar
Visualization
ISSN:0196-2892, 1558-0644
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Zusammenfassung:In this article, we aim to improve the performance of synthetic aperture radar (SAR) ship detection under complex conditions. The complex backgrounds are commonly encountered for high-resolution (HR) SAR ship detection data set, and they greatly influence the detection performance of ships. In recent years, deep neural networks (DNNs) have made substantial improvements on detection by adopting data augmentation. However, the improvement is limited since the models are sensitive to noise. To address this problem, a Fast Saliency-based Filtering algorithm (FSF) is proposed to filter out interference information. The FSF method is inspired by the filtering mechanisms of the human brain, which help people filter out target-irrelevant information fast to better extract target-relevant information. The FSF includes two parts of the bottom-up process and the top-down process. The bottom-up process is used to extract a saliency map of an input image, and the other one is used to filter out target-irrelevant information based on the saliency map. The FSF can be a front-end preprocessing module of DNNs to fast filter out target-irrelevant information and obtain a primary priority map of an input image. Experimental results demonstrate that our brain-inspired FSF method obtains obvious improvement of detection performance on AIR-SARShip-1.0.
Bibliographie:ObjectType-Article-1
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content type line 14
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2021.3053257