Inverse Synthetic Aperture Radar Image Quality Assessment Based on BP Neural Network

Based on back propagation (BP) neural network, a novel inverse synthetic aperture radar (ISAR) image quality assessment (IQA) method was proposed to address the challenge of selecting high-quality ISAR images from a large number of images. This method incorporates six assessment metrics: energy gradient (EG), effective area ratio of image (EARI), image contrast (IC), image entropy (IE), equivalent number of looks (ENL), and signal-to-noise ratio (SNR) to evaluate the quality of ISAR images. The BP neural network is employed to fit the relationship between these metrics and ISAR image quality. Experimental results show that the new method is effective in ISAR-IQA under different imaging conditions and various attitudes, and closely matches the subjective evaluation of experts.