Synthetic aperture radar (SAR) image classification is a fundamental research direction in image interpretation. With the development of various intelligent technologies, deep learning techniques are gradually being applied to SAR image classification. In this study, a new SAR classification algorithm known as the multiscale convolutional neural network with an autoencoder regularization joint contextual attention network (MCAR-CAN) is proposed. The MCAR-CAN has two branches: the autoencoder regularization branch and the context attention branch. First, autoencoder regularization is used for the reconstruction of the input to regularize the classification in the autoencoder regularization branch. Multiscale input and an asymmetric structure of the autoencoder branch cause the network more to be focused on classification than on reconstruction. Second, the attention mechanism is used to produce an attention map in which each attention weight corresponds to a context correlation in attention branch. The robust features are obtained by the attention mechanism. Finally, the features obtained by the two branches are spliced for classification. In addition, a new training strategy and a postprocessing method are designed to further improve the classification accuracy. Experiments performed on the data from three SAR images demonstrated the effectiveness and robustness of the proposed algorithm.

中文翻译：

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用于 SAR 图像分类的具有自动编码器正则化联合上下文注意网络的多尺度 CNN


合成孔径雷达（SAR）图像分类是图像判读的一个基础研究方向。随着各种智能技术的发展，深度学习技术逐渐应用于SAR图像分类。在这项研究中，提出了一种新的 SAR 分类算法，称为带有自动编码器正则化联合上下文注意网络的多尺度卷积神经网络（MCAR-CAN）。 MCAR-CAN 有两个分支：自动编码器正则化分支和上下文注意分支。首先，自动编码器正则化用于重建输入，以正则化自动编码器正则化分支中的分类。多尺度输入和自动编码器分支的不对称结构导致网络更关注分类而不是重建。其次，注意力机制用于生成注意力图，其中每个注意力权重对应于注意力分支中的上下文相关性。鲁棒的特征是通过注意力机制获得的。最后将两个分支得到的特征进行拼接进行分类。此外，还设计了新的训练策略和后处理方法，以进一步提高分类精度。对三幅 SAR 图像数据进行的实验证明了该算法的有效性和鲁棒性。