Application of convolutional neural networks trained on optical images for object detection in radar images

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 possibil...

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Published in:Kompʹûternaâ optika Vol. 48; no. 2; pp. 253 - 259
Main Authors: Pavlov, V.A., Belov, A.A., Volvenko, S.V., Rashich, A.V.
Format: Journal Article
Language:English
Published: 01-04-2024
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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.
AbstractList 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.
Author Belov, A.A.
Volvenko, S.V.
Pavlov, V.A.
Rashich, A.V.
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  fullname: Rashich, A.V.
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CorporateAuthor Peter the Great St.Petersburg Polytechnic University
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Title Application of convolutional neural networks trained on optical images for object detection in radar images
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