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Enhanced Feature Extraction for Ship Detection from Multi-Resolution and Multi-Scene Synthetic Aperture Radar (SAR) Images

by 1, 1, 1,*, 2 and 3
1
Electronic Information Engineering, Beihang University, Beijing 100191, China
2
Space Mechatronic Systems Technology Laboratory, Department of Design, Manufacture and Engineering, Management, University of Strathclyde, Glasgow G11XJ, UK
3
Department of Informatics, University of Leicester, Leicester LE1 7RH, UK
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(22), 2694; https://doi.org/10.3390/rs11222694
Received: 1 October 2019 / Revised: 11 November 2019 / Accepted: 15 November 2019 / Published: 18 November 2019
(This article belongs to the Section Ocean Remote Sensing)
Independent of daylight and weather conditions, synthetic aperture radar (SAR) images have been widely used for ship monitoring. The traditional methods for SAR ship detection are highly dependent on the statistical models of sea clutter or some predefined thresholds, and generally require a multi-step operation, which results in time-consuming and less robust ship detection. Recently, deep learning algorithms have found wide applications in ship detection from SAR images. However, due to the multi-resolution imaging mode and complex background, it is hard for the network to extract representative SAR target features, which limits the ship detection performance. In order to enhance the feature extraction ability of the network, three improvement techniques have been developed. Firstly, multi-level sparse optimization of SAR image is carried out to handle clutters and sidelobes so as to enhance the discrimination of the features of SAR images. Secondly, we hereby propose a novel split convolution block (SCB) to enhance the feature representation of small targets, which divides the SAR images into smaller sub-images as the input of the network. Finally, a spatial attention block (SAB) is embedded in the feature pyramid network (FPN) to reduce the loss of spatial information, during the dimensionality reduction process. In this paper, experiments on the multi-resolution SAR images of GaoFen-3 and Sentinel-1 under complex backgrounds are carried out and the results verify the effectiveness of SCB and SAB. The comparison results also show that the proposed method is superior to several state-of-the-art object detection algorithms. View Full-Text
Keywords: Ship Detection; Feature Enhancement; Split Convolution Block (SCB); Spatial Attention Block (SAB) Ship Detection; Feature Enhancement; Split Convolution Block (SCB); Spatial Attention Block (SAB)
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MDPI and ACS Style

Gao, F.; Shi, W.; Wang, J.; Yang, E.; Zhou, H. Enhanced Feature Extraction for Ship Detection from Multi-Resolution and Multi-Scene Synthetic Aperture Radar (SAR) Images. Remote Sens. 2019, 11, 2694. https://doi.org/10.3390/rs11222694

AMA Style

Gao F, Shi W, Wang J, Yang E, Zhou H. Enhanced Feature Extraction for Ship Detection from Multi-Resolution and Multi-Scene Synthetic Aperture Radar (SAR) Images. Remote Sensing. 2019; 11(22):2694. https://doi.org/10.3390/rs11222694

Chicago/Turabian Style

Gao, Fei, Wei Shi, Jun Wang, Erfu Yang, and Huiyu Zhou. 2019. "Enhanced Feature Extraction for Ship Detection from Multi-Resolution and Multi-Scene Synthetic Aperture Radar (SAR) Images" Remote Sensing 11, no. 22: 2694. https://doi.org/10.3390/rs11222694

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