Object Detection Research of SAR Image Using Improved Faster Region-Based Convolutional Neural Network

作者：Long SUN,Tao WU,Guangcai SUN,Dazheng FENG,Lieshu TONG,Mengdao XING

摘要：Target detection technology of synthetic aperture radar(SAR)imageis widely used in the field of military reconnaissance and surveillance.The traditional SAR image target detection methods need to be provided a lot of empirical knowledge because the characteristics of SAR images in different configurations(attitude,pitch angle,imaging parameters,etc.)will change greatly,resulting in high generalization error.Currently,deep learning method has achieved great success in the field of image processing.Research shows that deep learning can achieve a more intrinsic description of the data,while the model has a stronger ability of modeling and generalization.In order to solve the problem of insufficient data in SAR data sets,an experimental system for acquiring SAR image data in real scenes was built.Then the transfer learning method and the improved convolution neural network algorithm(PCA+Faster R-CNN)are applied to improve the target detection precision.Finally,experimental results demonstrate the significant effectiveness of the proposed method.

發文機構：National Lab of Radar Signal Processing Collaborative Innovation Center of Information Sensing and Understanding No.38 Research Institute of CETC

關鍵詞：targetdetectionSARimagedeeplearningtransferlearningPCA+FasterR-CNN

分類號： TN9[電子電信—信息與通信工程]