Ship detection in synthetic aperture radar (SAR) images by deep learning

Date

2019-01-01

Author

Ayhan, Oner
Sen, Nigar

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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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.

Subject Keywords

Ship detection, CNNs, SAR

URI

https://hdl.handle.net/11511/66161

DOI

https://doi.org/10.1117/12.2532781

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Unclassified, Conference / Seminar

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Citation Formats

O. Ayhan and N. Sen, “Ship detection in synthetic aperture radar (SAR) images by deep learning,” 2019, vol. 11169, p. 0, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/66161.