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SAR image classification based on CNN in real and simulation datasets

机译：真实和仿真数据集中基于CNN的SAR图像分类

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Convolution neural network (CNN) has made great success in image classification tasks. Even in the field of synthetic aperture radar automatic target recognition (SAR-ATR). state-of-art results has been obtained by learning deep representation of features on the MSTAR benchmark. However, the raw data of MSTAR have shortcomings in training a SAR-ATR model because of high similarity in background among the SAR images of each kind. This indicates that the CNN would learn the hierarchies of features of backgrounds as well as the targets. To validate the influence of the background, some other SAR images datasets have been made which contains the simulation SAR images of 10 manufactured targets such as tank and fighter aircraft, and the backgrounds of simulation SAR images are sampled from the whole original MSTAR data. The simulation datasets contain the dataset that the backgrounds of each kind images correspond to the one kind of backgrounds of MSTAR targets or clutters and the dataset that each image shares the random background of whole MSTAR targets or clutters. In addition, mixed datasets of MSTAR and simulation datasets had been made to use in the experiments. The CNN architecture proposed in this paper are trained on all datasets mentioned above. The experimental results shows that the architecture can get high performances on all datasets even the backgrounds of the images are miscellaneous, which indicates the architecture can learn a good representation of the targets even though the drastic changes on background.
机译：卷积神经网络（CNN）在图像分类任务中取得了巨大的成功。即使在合成孔径雷达自动目标识别（SAR-ATR）领域。通过学习MSTAR基准上功能的深入表示，可以获得最新的结果。然而，由于各种SAR图像的背景高度相似，因此MSTAR的原始数据在训练SAR-ATR模型方面存在缺陷。这表明CNN将学习背景特征以及目标的层次结构。为了验证背景的影响，已经制作了一些其他SAR图像数据集，其中包含10个制造目标的模拟SAR图像，例如坦克和战斗机，并且从整个原始MSTAR数据中采样了模拟SAR图像的背景。模拟数据集包含每种图像的背景与MSTAR目标或杂波的一种背景相对应的数据集，以及每个图像共享整个MSTAR目标或杂波的随机背景的数据集。此外，已将MSTAR的混合数据集和模拟数据集用于实验。本文提出的CNN架构已针对上述所有数据集进行了训练。实验结果表明，即使背景杂乱，该架构也可以在所有数据集上获得较高的性能，这表明即使背景发生了巨大变化，该架构也可以很好地学习目标。

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