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Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images
by
Ding, Zhiquan
, Zhou, Yuanyuan
, Chen, Zhe
, Wang, Xiaoting
, Zhang, Xiaoling
in
Clutter
/ coasts
/ Comparative analysis
/ data collection
/ Deep learning
/ False alarms
/ Harbor facilities
/ Harbors
/ Identification and classification
/ image analysis
/ Image processing
/ Image segmentation
/ inshore scene
/ Methods
/ metrology
/ Military applications
/ Morphology
/ multi-modality saliency
/ Noise
/ Pixels
/ Port facilities
/ Radar
/ Radar detection
/ Radar imaging
/ Remote sensing
/ Salience
/ ship detection
/ Ships
/ Spatial distribution
/ surface metrology
/ Synthetic aperture radar
/ synthetic aperture radar (SAR)
2023
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Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images
by
Ding, Zhiquan
, Zhou, Yuanyuan
, Chen, Zhe
, Wang, Xiaoting
, Zhang, Xiaoling
in
Clutter
/ coasts
/ Comparative analysis
/ data collection
/ Deep learning
/ False alarms
/ Harbor facilities
/ Harbors
/ Identification and classification
/ image analysis
/ Image processing
/ Image segmentation
/ inshore scene
/ Methods
/ metrology
/ Military applications
/ Morphology
/ multi-modality saliency
/ Noise
/ Pixels
/ Port facilities
/ Radar
/ Radar detection
/ Radar imaging
/ Remote sensing
/ Salience
/ ship detection
/ Ships
/ Spatial distribution
/ surface metrology
/ Synthetic aperture radar
/ synthetic aperture radar (SAR)
2023
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Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images
by
Ding, Zhiquan
, Zhou, Yuanyuan
, Chen, Zhe
, Wang, Xiaoting
, Zhang, Xiaoling
in
Clutter
/ coasts
/ Comparative analysis
/ data collection
/ Deep learning
/ False alarms
/ Harbor facilities
/ Harbors
/ Identification and classification
/ image analysis
/ Image processing
/ Image segmentation
/ inshore scene
/ Methods
/ metrology
/ Military applications
/ Morphology
/ multi-modality saliency
/ Noise
/ Pixels
/ Port facilities
/ Radar
/ Radar detection
/ Radar imaging
/ Remote sensing
/ Salience
/ ship detection
/ Ships
/ Spatial distribution
/ surface metrology
/ Synthetic aperture radar
/ synthetic aperture radar (SAR)
2023
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Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images
Journal Article
Inshore Ship Detection Based on Multi-Modality Saliency for Synthetic Aperture Radar Images
2023
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Overview
Synthetic aperture radar (SAR) ship detection is of significant importance in military and commercial applications. However, a high similarity in intensity and spatial distribution of scattering characteristics between the ship target and harbor facilities, along with a fuzzy sea-land boundary due to the strong speckle noise, result in a low detection accuracy and high false alarm rate for SAR ship detection with complex inshore scenes. In this paper, a new inshore ship detection method based on multi-modality saliency is proposed to overcome these challenges. Four saliency maps are established from different perspectives: an ocean-buffer saliency map (OBSM) outlining more accurate coastline under speckle noises; a local stability saliency map (LSSM) addressing pixel spatial distribution; a super-pixel saliency map (SPSM) extracting critical region-based features for inshore ship detection; and an intensity saliency map (ISM) to highlight target pixels with intensity distribution. By combining these saliency maps, ship targets in complex inshore scenes can be successfully detected. The method provides a novel interdisciplinary perspective (surface metrology) for SAR image segmentation, discovers the difference in spatial characteristics of SAR image elements, and proposes a novel robust CFAR procedure for background clutter fitting. Experiments on a public SAR ship detection dataset (SSDD) shows that our method achieves excellent detection performance, with a low false alarm rate, in offshore scenes, inshore scenes, inshore scenes with confusing metallic port facilities, and large-scale scenes. The results outperform several widely used methods, such as CFAR-based methods and super-pixel methods.
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