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

• 作 者: Long SUN Tao WU Guangcai SUN Dazheng FENG Lieshu TONG Mengdao XING
• 机 构: National Lab of Radar Signal Processing, Xidian University, Xi’ an 710071, China“,”Collaborative Inn
• 出 处: 测绘学报（英文版）
• 发表日期: 2020年3期
• 关键词: target detection SAR image deep learning transfer learning PCA+Faster R-CNN

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 leing method has achieved great success in the field of image processing. Research shows that deep leing 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 leing 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.