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marine target detection based on marine-Faster R-CNN for navigation radar plane position indicator images

Frontiers of Information Technology & Electronic Engineering 2022年第4期23卷 630-643页

作者： Xiaolong CHEN Xiaoqian MU Jian GUAN Ningbo LIU Wei ZHOU marine target detection Research Group Naval Aviation UniversityYantai264001China

As a classic deep learning target detection algorithm,Faster R-CNN(region convolutional neural network)has been widely used in high-resolution synthetic aperture radar(SAR)and inverse SAR(ISAR)image ***,for most common low-resolution radar plane position indicator(PPI)images,it is difficult to achieve good *** this paper,taking navigation radar PPI images as an example,a marine target detection method based on the marine-Faster R-CNN algorithm is proposed in the case of complex background(e.g.,sea clutter)and target *** method performs feature extraction and target recognition on PPI images generated by radar echoes with the convolutional neural network(CNN).First,to improve the accuracy of detecting marine targets and reduce the false alarm rate,Faster R-CNN was optimized as the marine-Faster R-CNN in five respects:new backbone network,anchor size,dense target detection,data sample balance,and scale ***,JRC(Japan Radio Co.,Ltd.)navigation radar was used to collect echo data under different conditions to build a marine target ***,comparisons with the classic Faster R-CNN method and the constant false alarm rate(CFAR)algorithm proved that the proposed method is more accurate and robust,has stronger generalization ability,and can be applied to the detection of marine targets for navigation *** performance was tested with datasets from different observation conditions(sea states,radar parameters,and different targets).

关键词： marine target detection Navigation radar Plane position indicator(PPI)images Convolutional neural network(CNN) Faster R-CNN(region convolutional neural network)method

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marine target detection Using Dual-polarimetric SAR Imagery

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Journal of Geodesy and Geoinformation Science 2021年第1期4卷 63-69页

作者： Tao ZHANG Armando MARINO Ferdinando NUNZIATA Domenico VELOTTO Weizeng SHAO Xiaofeng LI Maurizio MIGLIACCIO Huilin XIONG Department of Electronic Engineering Tsinghua UniversityBeijing 100084China The University of Stirling Natural SciencesStirling FK94LAU.K. Universitàdegli Studi di Napoli Parthenope Napoli 38-80133Italy German Aerospace Center Bremen 28199Germany Colleage of marine Sciences Shanghai Ocean UniversityShanghai 201306China Institute of Oceanology Chinese Academy of SciencesNanhai RoadQingdao 266071China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200240China

In this study,we provide a summary of research advances in the field of maritime target detection using DP(dualpolarimetric)SAR(Synthetic Aperture Radar)imagery,accomplished during the European and China collaboration in the framework of the Dragon-4 program ID *** main innovative contribution is twofold:(1)We addressed ship detection proposing an improved GP-PNF(Geometrical Perturbation-Polarimetric Notch Filter),termed as IGP-PNF,that is characterized by a new feature vector that includes three new scattering features;(2)We addressed oil platform detection by contrasting singlepolarization SAR methods with polarimetric ones in order to quantify the extra-benefit carried on polarimetric *** proposed theoretical framework is tested against actual multi-polarization SAR *** particular,ship detection methods are verified against a Sentinel-1 SAR scene where a large number of ships is present;while,oil platform detection is discussed using Terra SAR-X SAR *** analysis shows that:(1)The IGP-PNF method performs best in terms of clutter-to-target ratio;(2)Coherent polarimetric information significantly outperforms single-polarization SAR measurements in highlighting targets in challenging cases.

关键词： marine target detection dual-polarimetric SAR GP-PNF PCA Sentinel-1 TS-X

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