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19 September 2019 Ship detection in synthetic aperture radar (SAR) images by deep learning
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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.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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);

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