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Application of convolutional neural networks trained on optical images for object detection in radar images
V.A. Pavlov 1, A.A. Belov 1, S.V. Volvenko 1, A.V. Rashich 1

1 Peter the Great St.Petersburg Polytechnic University,
195251, Russia, St.Petersburg, Polytechnicheskaya 29

 PDF, 1800 kB

DOI: 10.18287/2412-6179-CO-1316

Pages: 253-259.

Full text of article: Russian language.

Abstract:
Due to the small number of annotated radar image datasets, the use of optical images for training neural networks designed to detect objects in radar images seems promising. However, optical images have some significant differences from radar images and an experimental investigation of this possibility is required. In this work we investigate the applicability of such an approach and show that in the case of detection of ships good results can be achieved. In addition, it is shown that preliminary filtering of speckle noise can improve the results.

Keywords:
speckle noise, radar image, SAR, noise reduction, image processing, SSIM, GMSD, object detection, neural networks.

Citation:
Pavlov VА, Belov AА, Volvenko SV, Rashich AV. Application of convolutional neural networks trained on optical images for object detection in radar images. Computer Optics 2024; 48(2): 253-259. DOI: 10.18287/2412-6179-CO-1316.

Acknowledgements:
The research was funded by the Ministry of Education and Science of the Russian Federation under grant # 075-15-2022-311 awarded from the federal budget for creation and development of world-class scientific centers working in advanced research and technology areas" of April 20, 2022.

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