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Proceedings Paper

Ship detection in synthetic aperture radar (SAR) images by deep learning
Author(s): Öner Ayhan; Nigar Şen
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Paper Abstract

In this paper, we propose a Convolutional Neural Network (CNN) based method to detect ships in Synthetic Aperture Radar (SAR) images. The architecture of proposed CNN has customized parts to detect small targets. In order to train, validate and test the CNN, TerraSAR-X Spot mode images are used. In the phase of data preparation, a GIS (Geographic Information System) specialist labels ships manually in all images. Later, image patches that contain ships are cropped and ground truths are also obtained from pre-labeled data. In the stage of train, data augmentation is used and the data divided into three parts: (i) train, (ii) validation, (iii) test. The training takes almost a day of duration with a NVIDIA GTX 1080 Ti graphic card. Results on test data shows that our method has promising detection performance for the ship targets on both open water and near harbors.

Paper Details

Date Published: 19 September 2019
PDF: 4 pages
Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 1116906 (19 September 2019);
Show Author Affiliations
Öner Ayhan, Space and Defence Technologies, METU Technopolis (Turkey)
Nigar Şen, Space and Defence Technologies, METU Technopolis (Turkey)


Published in SPIE Proceedings Vol. 11169:
Artificial Intelligence and Machine Learning in Defense Applications
Judith Dijk, Editor(s)

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