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

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journal contribution
posted on 30.04.2020, 09:36 by Fei Gao, Wei Shi, Jun Wang, Erfu Yang, Huiyu Zhou
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.

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61771027, Grant 61071139, Grant 61471019, Grant 61501011, and Grant 61171122. The work of H. Zhou was supported in part by the U.K. EPSRC under Grant EP/N508664/1, Grant EP/R007187/1, and Grant EP/N011074/1, and in part by the Royal Society-Newton Advanced Fellowship under Grant NA160342.

History

Citation

Remote Sensing, 2019, Volume 11, Issue 22

Author affiliation

Department of Informatics

Version

AM (Accepted Manuscript)

Published in

Remote Sensing

Volume

11

Issue

22

Pagination

2694 - 2694

Publisher

MDPI

issn

2072-4292

eissn

2072-4292

Acceptance date

15/11/2019

Copyright date

2019

Publisher version

https://www.mdpi.com/2072-4292/11/22/2694

Language

en