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Open AccessArticle

Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images

1
Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi’an 710071, China
2
National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China
3
School of Electronics and Information, Northwestern Polytechnical University, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2862; https://doi.org/10.3390/rs11232862
Received: 9 October 2019 / Revised: 22 November 2019 / Accepted: 27 November 2019 / Published: 2 December 2019
Ship detection plays an important role in many remote sensing applications. However, the performance of the PolSAR ship detection may be degraded by the complicated scattering mechanism, multi-scale size of targets, and random speckle noise, etc. In this paper, we propose a ship detection method for PolSAR images based on modified faster region-based convolutional neural network (Faster R-CNN). The main improvements include proposal generation by adopting multi-level features produced by the convolution layers, which fits ships with different sizes, and the addition of a Deep Convolutional Neural Network (DCNN)-based classifier for training sample generation and coast mitigation. The proposed method has been validated by four measured datasets of NASA/JPL airborne synthetic aperture radar (AIRSAR) and uninhabited aerial vehicle synthetic aperture radar (UAVSAR). Performance comparison with the modified constant false alarm rate (CFAR) detector and the Faster R-CNN has demonstrated that the proposed method can improve the detection probability while reducing the false alarm rate and missed detections. View Full-Text
Keywords: Polarimetric synthetic aperture radar (PolSAR); ship detection; deep convolutional neural network (DCNN) Polarimetric synthetic aperture radar (PolSAR); ship detection; deep convolutional neural network (DCNN)
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MDPI and ACS Style

Fan, W.; Zhou, F.; Bai, X.; Tao, M.; Tian, T. Ship Detection Using Deep Convolutional Neural Networks for PolSAR Images. Remote Sens. 2019, 11, 2862.

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