中国科学院大学学报2018，Vol.35Issue(1)：75-83,9.DOI:10.7523/j.issn.2095-6134.2018.01.010
深度卷积神经网络在迁移学习模式下的SAR目标识别
Target recognition using the transfer learning-based deep convolutional neural networks for SAR images
摘要
Abstract
The automatic target recognition procedure of synthetic aperture radar (SAR) generally includes two steps,feature extraction and classifier training.Based on the development of deep convolutional neural networks,we present a new method of SAR target recognition.This method automatically learns the hierarchies of features from different targets,which means it avoids the nonnormalization caused by manual feature extraction.Then the transfer learning technology is applied to avert the occurrence of locally optimal solution and accelerate the training procedure.Finally we use the moving and stationary target acquisition and recognition database to verify our method.关键词
合成孔径雷达(SAR)/自动目标识别/深度卷积神经网络/迁移学习Key words
synthetic aperture radar (SAR)/automatic target recognition (ATR)/deep convolutional neural networks (DNNs)/transfer learning分类
信息技术与安全科学引用本文复制引用
李松,魏中浩,张冰尘,洪文..深度卷积神经网络在迁移学习模式下的SAR目标识别[J].中国科学院大学学报,2018,35(1):75-83,9.基金项目
国家自然科学基金(61571419)资助 （61571419）
