Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images

Synthetic aperture radar (SAR) is an active microwave imaging sensor with the capability of working in all-weather, all-day to provide high-resolution SAR images. Recently, SAR images have been widely used in civilian and military fields, such as ship detection. The scales of different ships vary in...

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Vydáno v:	IEEE transactions on geoscience and remote sensing Ročník 57; číslo 11; s. 8983 - 8997
Hlavní autoři:	Cui, Zongyong, Li, Qi, Cao, Zongjie, Liu, Nengyuan
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	New York IEEE 01.11.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:	Accuracy Datasets Dense attention pyramid network (DAPN) Detection Detection algorithms Feature extraction Feature maps Image detection Image resolution Imaging techniques Marine vehicles Microwave imaging multi-scale feature maps Multiscale analysis Radar imaging Radar polarimetry Resolution SAR (radar) Semantics ship detection Ships Synthetic aperture radar synthetic aperture radar (SAR) Weather
ISSN:	0196-2892, 1558-0644
On-line přístup:	Získat plný text
Tagy:	Přidat tag Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!

Abstract	Synthetic aperture radar (SAR) is an active microwave imaging sensor with the capability of working in all-weather, all-day to provide high-resolution SAR images. Recently, SAR images have been widely used in civilian and military fields, such as ship detection. The scales of different ships vary in SAR images, especially for small-scale ships, which only occupy few pixels and have lower contrast. Compared with large-scale ships, the current ship detection methods are insensitive to small-scale ships. Therefore, the ship detection methods are facing difficulties with multi-scale ship detection in SAR images. A novel multi-scale ship detection method based on a dense attention pyramid network (DAPN) in SAR images is proposed in this paper. The DAPN adopts a pyramid structure, which densely connects convolutional block attention module (CBAM) to each concatenated feature map from top to bottom of the pyramid network. In this way, abundant features containing resolution and semantic information are extracted for multi-scale ship detection while refining concatenated feature maps to highlight salient features for specific scales by CBAM. Then, the salient features are integrated with global unblurred features to improve accuracy effectively in SAR images. Finally, the fused feature maps are fed to the detection network to obtain the final detection results. Experiments on the data set of SAR ship detection data set (SSDD) including multi-scale ships in various SAR images show that the proposed method can detect multi-scale ships in different scenes of SAR images with extremely high accuracy and outperforms other ship detection methods implemented on SSDD.
AbstractList	Synthetic aperture radar (SAR) is an active microwave imaging sensor with the capability of working in all-weather, all-day to provide high-resolution SAR images. Recently, SAR images have been widely used in civilian and military fields, such as ship detection. The scales of different ships vary in SAR images, especially for small-scale ships, which only occupy few pixels and have lower contrast. Compared with large-scale ships, the current ship detection methods are insensitive to small-scale ships. Therefore, the ship detection methods are facing difficulties with multi-scale ship detection in SAR images. A novel multi-scale ship detection method based on a dense attention pyramid network (DAPN) in SAR images is proposed in this paper. The DAPN adopts a pyramid structure, which densely connects convolutional block attention module (CBAM) to each concatenated feature map from top to bottom of the pyramid network. In this way, abundant features containing resolution and semantic information are extracted for multi-scale ship detection while refining concatenated feature maps to highlight salient features for specific scales by CBAM. Then, the salient features are integrated with global unblurred features to improve accuracy effectively in SAR images. Finally, the fused feature maps are fed to the detection network to obtain the final detection results. Experiments on the data set of SAR ship detection data set (SSDD) including multi-scale ships in various SAR images show that the proposed method can detect multi-scale ships in different scenes of SAR images with extremely high accuracy and outperforms other ship detection methods implemented on SSDD.
Author	Cao, Zongjie Cui, Zongyong Li, Qi Liu, Nengyuan
Author_xml	– sequence: 1 givenname: Zongyong orcidid: 0000-0003-1155-786X surname: Cui fullname: Cui, Zongyong email: zycui@uestc.edu.cn organization: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Qi surname: Li fullname: Li, Qi email: lucialee0103@gmail.com organization: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 3 givenname: Zongjie orcidid: 0000-0002-0117-9087 surname: Cao fullname: Cao, Zongjie email: zjcao@uestc.edu.cn organization: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China – sequence: 4 givenname: Nengyuan orcidid: 0000-0002-2827-3321 surname: Liu fullname: Liu, Nengyuan email: nengyuanliu@outlook.com organization: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
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CODEN	IGRSD2
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SubjectTerms	Accuracy Datasets Dense attention pyramid network (DAPN) Detection Detection algorithms Feature extraction Feature maps Image detection Image resolution Imaging techniques Marine vehicles Microwave imaging multi-scale feature maps Multiscale analysis Radar imaging Radar polarimetry Resolution SAR (radar) Semantics ship detection Ships Synthetic aperture radar synthetic aperture radar (SAR) Weather
Title	Dense Attention Pyramid Networks for Multi-Scale Ship Detection in SAR Images
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