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

被引：0

作者：

Pavlov, V. A. ^{[1
,2
]}

Belov, A. A. ^{[1
,2
]}

Volvenko, S. V. ^{[1
,2
]}

Rashich, A., V ^{[1
,2
]}

机构：

[1] Peter Great St Petersburg Polytech Univ, Polytech Skaya 29, St Petersburg 195251, Russia

[2] Peter Great St Petersburg Polytech Univ, Inst Elect & Telecommun, St Petersburg, Russia

来源：

COMPUTER OPTICS | 2024年 / 48卷 / 02期

关键词：

speckle noise; radar image; SAR; noise reduction; image processing; SSIM; GMSD; object detection; neural networks;

D O I：

10.18287/2412-6179-CO-1316

中图分类号：

O43 [光学];

学科分类号：

070207 ; 0803 ;

摘要：

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.

引用

页码：253 / 259

页数：8

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