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

Öner Ayhan; Nigar Şen

doi:10.1117/12.2532781

19 September 2019 Ship detection in synthetic aperture radar (SAR) images by deep learning

Öner Ayhan, Nigar Şen

Proceedings Volume 11169, Artificial Intelligence and Machine Learning in Defense Applications; 1116906 (2019) https://doi.org/10.1117/12.2532781
Event: SPIE Security + Defence, 2019, Strasbourg, France

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.

Conference Presentation

Citation Download Citation

Öner Ayhan and Nigar Şen "Ship detection in synthetic aperture radar (SAR) images by deep learning", Proc. SPIE 11169, Artificial Intelligence and Machine Learning in Defense Applications, 1116906 (19 September 2019); https://doi.org/10.1117/12.2532781

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available