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Open AccessArticle

A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery

by Jun Wang 1, Tong Zheng 1, Peng Lei 1,* and Xiao Bai 2
1
School of Electronic and Information Engineering, Beihang University, Beijing 100191, China
2
School of Computer Science and Engineering, Beihang University, Beijing 100191, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(6), 620; https://doi.org/10.3390/rs11060620
Received: 29 January 2019 / Revised: 9 March 2019 / Accepted: 11 March 2019 / Published: 14 March 2019
The ghost phenomenon in synthetic aperture radar (SAR) imaging is primarily caused by azimuth or range ambiguities, which cause difficulties in SAR target detection application. To mitigate this influence, we propose a ship target detection method in spaceborne SAR imagery, using a hierarchical convolutional neural network (H-CNN). Based on the nature of ghost replicas and typical target classes, a two-stage CNN model is built to detect ship targets against sea clutter and the ghost. First, regions of interest (ROIs) were extracted from a large imaged scene during the coarse-detection stage. Unwanted ghost replicas represented major residual interference sources in ROIs, therefore, the other CNN process was executed during the fine-detection stage. Finally, comparative experiments and analyses, using Sentinel-1 SAR data and various assessment criteria, were conducted to validate H-CNN. Our results showed that the proposed method can outperform the conventional constant false-alarm rate technique and CNN-based models. View Full-Text
Keywords: spaceborne synthetic aperture radar; ship target detection; ghost; convolutional neural network spaceborne synthetic aperture radar; ship target detection; ghost; convolutional neural network
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Wang, J.; Zheng, T.; Lei, P.; Bai, X. A Hierarchical Convolution Neural Network (CNN)-Based Ship Target Detection Method in Spaceborne SAR Imagery. Remote Sens. 2019, 11, 620.

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