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SAR Target Detection Based on SSD With Data Augmentation and Transfer Learning

机译：基于SSD的SAR数据扩充和转移学习目标检测。

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摘要

In this letter, the single shot multibox detector (SSD), which is a real-time object detection method based on convolutional neural network, is applied to realize target detection for synthetic aperture radar (SAR) images. Since there are no sufficient labeled images for training in SAR target detection, we apply two strategies, data augmentation and transfer learning. For data augmentation, the first approaches to use some image processing methods, i.e., manual-extracting subimages, adding noise, filtering, and flipping, on the original training images to generate some new training images; the second approach is to employ the existing SAR target recognition data set, MSTAR data set, to assist in accomplishing the target detection task. For transfer learning, we first apply subaperture decomposition technique on original SAR images to acquire three-channel subaperture SAR images, and then transfer the three-channel VGGNet model pretrained on the ImageNet data set to the three-channel subaperture SAR images, in order to initialize corresponding parameters of the convolutional layers in the base network in our SSD. The feature extraction network, consisting of the base network and the auxiliary structure, is used to learn multiscale feature maps, and then convolutional predictors are used to acquire the final detection results. The experimental results on the miniSAR real image data set demonstrate that the proposed method can obtain better detection performance than other detection methods.
机译：本文以基于卷积神经网络的实时目标检测方法-单发多盒检测器（SSD）为研究对象，对合成孔径雷达（SAR）图像进行目标检测。由于没有足够的标记图像来训练SAR目标，因此我们采用两种策略，即数据增强和转移学习。对于数据增强，第一种方法是在原始训练图像上使用一些图像处理方法，即手动提取子图像，添加噪声，过滤和翻转以生成一些新的训练图像；第二种方法是利用现有的SAR目标识别数据集MSTAR数据集来协助完成目标检测任务。为了进行转移学习，我们首先在原始SAR图像上应用子孔径分解技术以获取三通道子孔径SAR图像，然后将对ImageNet数据集进行预训练的三通道VGGNet模型转移到三通道子孔径SAR图像中，以便在我们的SSD中初始化基础网络中卷积层的相应参数。特征提取网络由基础网络和辅助结构组成，用于学习多尺度特征图，然后使用卷积预测器获取最终的检测结果。在miniSAR真实图像数据集上的实验结果表明，与其他检测方法相比，该方法可以获得更好的检测性能。

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