摘要
真实场景的高分辨率合成孔径雷达(SAR)图像大多是复杂的,对于地物场景来说,其背景中存在草地、树木、道路和建筑物等杂波,这些复杂背景杂波使得传统SAR图像目标检测算法的结果包含大量虚警和漏警,严重影响了SAR目标检测性能。该文提出一种基于特征分解卷积神经网络(CNN)的SAR图像目标检测方法,该方法在特征提取模块对输入图像提取特征后,通过特征分解模块分解出鉴别特征和干扰特征,最后将鉴别特征输入到多尺度检测模块进行目标检测。特征分解后去除的干扰特征是对目标检测不利的部分,其中包括复杂背景杂波,而保留的鉴别特征是对目标检测有利的部分,其中包括感兴趣目标,从而有效降低虚警和漏警,提高SAR目标检测性能。该文所提方法在MiniSAR实测数据集和SAR飞机检测实测数据集(SADD)上的F1-score值分别为0.9357和0.9211,与不加特征分解模块的单步多框检测器相比,所提方法的F1-score值分别提升了0.0613和0.0639。基于实测数据集的实验结果证明了所提方法对复杂场景SAR图像进行目标检测的有效性。
Most high-resolution Synthetic Aperture Radar(SAR)images of real-life scenes are complex due to clutter,such as grass,trees,roads,and buildings,in the background.Traditional target detection algorithms for SAR images contain numerous false and missed alarms due to such clutter,adversely affecting the performance of SAR images target detection.Herein we propose a feature decomposition-based Convolutional Neural Network(CNN)for target detection in SAR images.The feature extraction module first extracts features from the input images,and these features are then decomposed into discriminative and interfering features using the feature decomposition module.Furthermore,only the discriminative features are input into the multiscale detection module for target detection.The interfering features that are removed after feature decomposition are the parts that are unfavorable to target detection,such as complex background clutter,whereas the discriminative features that are retained are the parts that are favorable to target detection,such as the targets of interest.Hence,an effective reduction in the number of false and missed alarms,as well as an improvement in the performance of SAR target detection,is achieved.The F1-score values of the proposed method are 0.9357 and 0.9211 for the MiniSAR dataset and SAR Aircraft Detection Dataset(SADD),respectively.Compared to the single shot multibox detector without the feature extraction module,the F1-score values of the proposed method for the MiniSAR and SADD datasets show an improvement of 0.0613 and 0.0639,respectively.Therefore,the effectiveness of the proposed method for target detection in SAR images of complex scenes was demonstrated through experimental results based on the measured datasets.
作者
李毅
杜兰
杜宇昂
LI Yi;DU Lan;DU Yuang(National Key Laboratory of Radar Signal Processing,Xidian University,Xi’an 710071,China)
出处
《雷达学报（中英文）》
EI
CSCD
北大核心
2023年第5期1069-1080,共12页
Journal of Radars
基金
国家自然科学基金(U21B2039)。
关键词
合成孔径雷达
卷积神经网络
目标检测
特征分解
鉴别特征
干扰特征
Synthetic Aperture Radar(SAR)
Convolutional Neural Network(CNN)
Target detection
Feature decomposition
Discriminative features
Interfering features
