大规模,多样化,和高质量的数据是实现基于深度学习的目标检测识别算法的良好泛化的基础和关键。然而,现有的合成孔径雷达(SAR)图像智能增强方法面临着几个问题,包括训练不稳定,图像质量较差,缺乏物理可解释性,等。为了解决上述问题,提出了一种特征级SAR目标数据增强方法。首先,提出了一种增强型胶囊神经网络(CapsNet),并将其用于特征提取,解耦输入数据的属性信息。此外,使用基于注意力机制的属性解耦框架,这有利于实现更有效的特征表示。之后,解耦的属性特征,包括振幅,仰角,方位角,和形状,可以被扰动以增加特征的多样性。在此基础上,通过重构扰动特征来实现SAR目标图像的增强。与使用随机噪声作为输入的增强方法相比,该方法实现了已知分布输入到未知分布变化的映射。这种映射方法减少了输入信号和增强数据之间的相关距离,因此减少了对训练数据的需求。此外,我们在重建过程中结合了像素损失和感知损失,提高了增广SAR数据的质量。使用四个评估度量来进行真实和增强图像的评估。通过该方法生成的图像实现了21.6845的峰值信噪比(PSNR),3.7114的辐射分辨率(RL)和24.0654的动态范围(DR)。实验结果证明了该方法的优越性。
Large-scale, diverse, and high-quality data are the basis and key to achieving a good generalization of target detection and recognition algorithms based on deep learning. However, the existing methods for the intelligent augmentation of synthetic aperture radar (SAR) images are confronted with several issues, including training instability, inferior image quality, lack of physical interpretability, etc. To solve the above problems, this paper proposes a feature-level SAR target-data augmentation method. First, an enhanced capsule neural network (CapsNet) is proposed and employed for feature extraction,
decoupling the attribute information of input data. Moreover, an attention mechanism-based attribute
decoupling framework is used, which is beneficial for achieving a more effective representation of features. After that, the decoupled attribute feature, including amplitude, elevation angle, azimuth angle, and shape, can be perturbed to increase the diversity of features. On this basis, the augmentation of SAR target images is realized by reconstructing the perturbed features. In contrast to the augmentation methods using random noise as input, the proposed method realizes the mapping from the input of known distribution to the change in unknown distribution. This mapping method reduces the correlation distance between the input signal and the augmented data, therefore diminishing the demand for training data. In addition, we combine pixel loss and perceptual loss in the reconstruction process, which improves the quality of the augmented SAR data. The evaluation of the real and augmented images is conducted using four assessment metrics. The images generated by this method achieve a peak signal-to-noise ratio (PSNR) of 21.6845, radiometric resolution (RL) of 3.7114, and dynamic range (DR) of 24.0654. The experimental results demonstrate the superior performance of the proposed method.