基于深度学习的复杂沙漠背景SAR目标检测
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摘要
SAR目标检测,因成像场景大、背景复杂多变而极具挑战。传统基于恒虚警率的SAR目标检测方法极易受背景干扰。针对上述问题,提出一种基于深度学习的复杂沙漠背景SAR目标端对端检测识别系统。即采用小规模沙漠背景下的SAR图像数据对Faster-RCNN网络进行迁移训练,一体化完成典型目标的检测与识别。基于合成数据集Desert-SAR的试验结果表明,与传统方法相比,该方法检测速度更快、准确率更高、鲁棒性更强。
Target detection in synthetic aperture radar(SAR)image is a challenge due to the large-scale and complex imaging scene.The classical methods based on CFAR are sensible to imaging scene.Aiming at this problem,we propose an end-to-end target detection method for SAR image in desert scene based on deep learning.That is,the transfer learning is employed to adjust the Faster-RCNN network for optical image to the SAR image.Experimental results of the Dessert-SAR data set show that the proposed method can achieve faster detection speed,higher accuracy and robustness compared with the classical ones.
Target detection in synthetic aperture radar(SAR)image is a challenge due to the large-scale and complex imaging scene.The classical methods based on CFAR are sensible to imaging scene.Aiming at this problem,we propose an end-to-end target detection method for SAR image in desert scene based on deep learning.That is,the transfer learning is employed to adjust the Faster-RCNN network for optical image to the SAR image.Experimental results of the Dessert-SAR data set show that the proposed method can achieve faster detection speed,higher accuracy and robustness compared with the classical ones.
引文
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[2]甄勇,刘伟,陈建宏,等.高分辨率SAR图像舰船目标几何结构特征提取[J].信号处理,2016,32(4):424-429.
[3]ZHAO Q,PRINCIPE J C.Support Vector Machines for SAR Automatic Target Recognition[J].IEEE Trans on Aerospace and Electronic Systems,2001,37(2):643-654.
[4]王思雨,高鑫,孙皓,等.基于卷积神经网络的高分辨率SAR图像飞机目标检测方法[J].雷达学报,2017,6(2):195-203.
[5]REN S,HE K M,GIRSHICK R,et al.Faster R-CNN:Towards Real-Time Object Detection with Region Proposal Networks[J].IEEE Trans on Pattern Analysis and Machine Intelligence,2015,39(6):1137-1147.
[6]吴冬茵,桂林,陈钊,等.基于深度表示学习和高斯过程迁移学习的情感分析方法[J].中文信息学报,2017,31(1):169-176.