A Joint SAR Ship Detection and Azimuth Estimation Method

LI Jianwei; QU Changwen; PENG Shujuan

doi:10.13203/j.whugis20170328

Volume 44 Issue 6

Jun. 2019

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Geomatics and Information Science of Wuhan University > 2019 > 44(6): 901-907. > DOI: 10.13203/j.whugis20170328

LI Jianwei, QU Changwen, PENG Shujuan. A Joint SAR Ship Detection and Azimuth Estimation Method[J]. Geomatics and Information Science of Wuhan University, 2019, 44(6): 901-907. DOI: 10.13203/j.whugis20170328

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A Joint SAR Ship Detection and Azimuth Estimation Method

1.
Naval Aeronautical University, Yantai 264001, China

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Author Bio:
LI Jianwei, PhD candidate, specializes in target detection and recognition in SAR images, deep learning and computer vision. E-mail: lgm_jw@163.com
Corresponding author:
QU Changwen, PhD, professoy. E-mail: qcwwby@sohu.com
Received Date: March 13, 2018
Published Date: June 04, 2019

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Abstract

Abstract

This paper proposes a fast single stage synthetic aperture radar(SAR) ship detection and azimuth estimation method. It can output the location, type and orientation of the object in the image after a forward process, which is completely end to end for training and inference. This method is based on the single shot detector(SSD). The feature pyramid network makes full use of the high-level semantic features and low-level position features, which make the bottom and top layers have class information. This can solve the following two problems:Small targets are easy ignored at the top layer and the bottom layer would predict the wrong class. The loss function reduces the weight of huge number easy classified examples, which can avoid dominating the hard classified examples. This can make the objective function converge better and faster. By adding the new azimuth estimation module, the method can perform the two tasks simultaneously with a small increase in calculation. By the experiments on the opened SAR ship detection dataset, we can find that the proposed method can detect ships and estimate the orientation rapidly and accurately.
- SAR,
- ship detection,
- feature fusion,
- azimuth estimation,
- end-to-end

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