In recent years, methods based on convolutional neural networks (CNNs) have achieved significant results in the problem of target classification of synthetic aperture radar (SAR) images. However, the challenges of SAR image data labeling and the characteristics of CNNs relying on a large amount of labeled data for training have seriously limited the further development of this field. In this work, we propose an approach based on attention mechanism and feature complementary fusion (AFCF-CNN) to address these challenges. First, we design and construct a feature complementary module for extracting and fusing multi-layer features, making full use of limited data and utilizing contextual information between different layers to capture more robust feature representations. Then, the attention mechanism reduces the interference of redundant background information, while it highlights the weight information of key targets in the image to further enhance the key local feature representations. Finally, experiments conducted on the moving and stationary target acquisition and recognition dataset show that our model significantly outperforms other state-of-the-art methods despite severe shortages of training data.

中文翻译：

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基于注意力和特征互补融合的深度神经网络用于小样本合成孔径雷达图像分类

近年来，基于卷积神经网络（CNN）的方法在合成孔径雷达（SAR）图像的目标分类问题上取得了显着的成果。然而，SAR图像数据标记的挑战以及CNN依赖大量标记数据进行训练的特点严重限制了该领域的进一步发展。在这项工作中，我们提出了一种基于注意力机制和特征互补融合（AFCF-CNN）的方法来应对这些挑战。首先，我们设计并构建了一个特征互补模块，用于提取和融合多层特征，充分利用有限的数据并利用不同层之间的上下文信息来捕获更鲁棒的特征表示。然后，注意力机制减少了冗余背景信息的干扰，同时突出了图像中关键目标的权重信息，进一步增强了关键的局部特征表示。最后，对移动和静止目标获取和识别数据集进行的实验表明，尽管训练数据严重短缺，我们的模型仍显着优于其他最先进的方法。