基于深度学习的复杂沙漠背景SAR目标检测

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。

SAR Target Detection in Complex Desert Background Images Based on Deep Learning

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.