From Coarse to Fine: ISAR Object View Interpolation via Flow Estimation and GAN

The paper focuses on the multi-azimuth interpolation task of inverse synthetic aperture radar (ISAR) images for aircraft targets and complements incomplete ISAR image datasets. ISAR image automatic target recognition (ATR) has been widely applied in remote sensing and many fields. However, the imaging process is more challenging when compared to capturing optical and SAR image data, which reduces the accuracy and generalization performance of the ATR system. Therefore, in this paper, we leverage existing limited ISAR data to achieve autonomous data expansion. This approach helps mitigate the impact of low sample quantity and unbalanced distribution, ultimately improving the accuracy of the ATR system for target recognition. Most existing methods use generative networks for ISAR image expansion, but few focus on generating ISAR images with specific azimuths. The paper proposes a novel two-stage coarse-to-fine framework for ISAR object view interpolation (C2FIPNet) that combines flow estimation and GAN to interpolate ISAR images with intermediate azimuths using a set of ISAR image pairs. Flow estimation is employed for coarsegrained generation, determining the position and intensity of strong scattering points in the ISAR image. The GAN, on the other hand, is used for fine-grained completion to correct image distortion caused by flow estimation and enhance image details. Additionally, a suitable loss function is designed, incorporating both global and local features, allowing for priority generation in the region of strong scattering points. In conclusion, extensive simulation and comparative experiments have demonstrated that the interpolated ISAR images generated by the proposed C2FIPNet exhibit greater pixel-level authenticity.