Curvelet Adversarial Augmented Neural Network for SAR Image Classification

Convolutional neural networks (CNNs) have superior feature learning capabilities with large numbers of labeled samples. The reality is that labeling these samples is costly in terms of human labor. Existing data augmentation methods alleviate the scarcity of labeled samples. However, these methods are not suitable for synthetic aperture radar (SAR) images, owing to special imaging mechanisms and observational objects. The generative SAR images by existing augmented methods show structure distortion. To address this issue, we introduce a curvelet adversarial augmented neural network (CA2NN) for SAR image classification. Specifically, an
$\text{A}^{2}$
NN is established, which consists of two generative streams and one discriminative stream. In the generative stream, through the mutual transformation between the whole and partial images, more new samples with structural consistency are generated to augment the limited labeled data. In the discriminative stream, these generated samples show certain appearance variations after adversarial training based on the novel joint discriminant criterion. Simultaneously, given the multiscale and multidirectional nature of SAR images, we construct discretized curvelet in 2-D space, aiming to extract the singularity features and avoid overfitting. By integrating curvelet kernels into
$\text{A}^{2}$
NN, CA2NN can automatically generate more representative features adapting to complex terrain, while greatly reducing the complexity of the network. Experiments are conducted on the SAR images with large-scale and complex scenes, suggesting that the proposed approach significantly improves the classification performance with few labeled samples.