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 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
01-04-2024
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| Online Access: | Get full text |
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| Summary: | 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. |
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| ISSN: | 0134-2452 2412-6179 |
| DOI: | 10.18287/2412-6179-CO-1316 |