Optimized complex object classification model: reconstructing the ISAR image of a hypersonic vehicle covered with a plasma sheath using a U-WGAN-GP framework

This study aims to apply generative adversarial networks (GANs) to the effective classification of high/low resolution (HR/LR) image pairs obtained via inverse synthetic aperture radar (ISAR) for hypersonic objects covered with a plasma sheath. We propose a classification training model based on a Wasserstein GAN with a gradient penalty (U-WGAN-GP) framework, wherein a U-Net with an excellent jump connection structure is used as a generator, and a VGG-Net with high robustness is used as a discriminator, to support the reliable classification of HR/LR ISAR image pairs for the enhancement of ISAR image resolution. The WGAN-GP provides a shortcut for the gradient propagation of network parameters during the training stage through the stable encoder and decoder structure of U-Net. It inherits the echo intensity variation and position distribution characteristics of the scattering points between hypersonic objects in LR images and effectively transplants them into the generated super-resolution ISAR images, and it establishes end-to-end mapping between the HR/LR ISAR images. Moreover, the VGG-Net ensures that the generated super-resolution images are stable, controllable, and undistorted. In addition, the WGAN-GP structure combines the content and adversarial losses, optimizes the generator loss function, and uses the GP method to provide a stable and continuous training process. Finally, a traditional VGG-16 network classifier is added at the end of the training model. The proposed model is applied to an HR/LR image pair dataset of hypersonic objects. Compared with existing methods, the proposed method improved the classification and recognition accuracy from 54.4% to 81.8%.