Learning discriminative features is difficult for deep learning-based target recognition in synthetic aperture radar (SAR) images with small training samples. To achieve a better feature learning, this study proposes a new deep network, a compact convolutional autoencoder (CCAE) for SAR target recognition. CCAE minimises the reconstruction loss and the distance between intra-class samples simultaneously by imposing compactness constraint on the encoder, which results in a more discriminative feature representation. Furthermore, the pretrained CCAE encoder can be used to initialise the corresponding parameters of a convolutional neural network to facilitate the training of the end-to-end model. Experimental results using the moving and stationary target acquisition and recognition dataset show that the proposed method outperforms the existing deep learning-based methods in the case of small training samples.

中文翻译：

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紧凑型卷积自动编码器，用于SAR目标识别

在具有小训练样本的合成孔径雷达（SAR）图像中，难以基于深度学习的目标学习识别特征。为了实现更好的特征学习，本研究提出了一种新的深度网络，一种用于SAR目标识别的紧凑型卷积自动编码器（CCAE）。CCAE通过在编码器上施加压缩约束，同时将重建损失和类内样本之间的距离最小化，从而获得更具区分性的特征表示。此外，预训练的CCAE编码器可用于初始化卷积神经网络的相应参数，以促进端到端模型的训练。