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Aircraft Detection in Sar Images Using Saliency Based Location Regression Network

机译：基于显着性的位置回归网络在Sar图像中的飞机检测

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摘要

In this paper, a novel framework for aircraft detection in high resolution apron area in Synthetic Aperture Radar (SAR) images is proposed, which combines the strength of location regression based convolutional neural network (CNN) framework and the salient features of target in SAR images. Specifically, a Constant False Alarm Rate (CFAR) based target pre-locating algorithm is introduced, which can match the scale of target in SAR images more accurate compared to the existing region proposal method. In addition, in order to eliminate the fact of overfitting, we explore several strategies for SAR data augmentation, including translation, adding noise and rotation within a small range. Experiments are conducted on the data set acquired by the TerraSAR-X satellite in a resolution of 3.0 meters. The results show that the proposed detection framework could effectively obtain a more accurate detection result.
机译：本文提出了一种新的合成孔径雷达（SAR）图像中高分辨率停机坪区域飞机检测框架，该框架结合了基于位置回归的卷积神经网络（CNN）框架的强度和目标在SAR图像中的显着特征。具体而言，引入了基于恒虚警率（CFAR）的目标预定位算法，与现有的区域提议方法相比，该算法可以更精确地匹配SAR图像中目标的比例。此外，为了消除过度拟合的事实，我们探索了几种用于SAR数据增强的策略，包括平移，增加噪声和在小范围内旋转。对TerraSAR-X卫星以3.0米的分辨率采集的数据集进行了实验。结果表明，提出的检测框架可以有效地获得更准确的检测结果。

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