SAR Image Quality Assessment Based on SSIM Using Textural Feature

The Synthetic Aperture Radar (SAR) image quality assessment (IQA) can provide a measurement for SAR jamming effect, which is helpful to improve the jamming pattern. A texture-based SSIM (TSSIM) algorithm is proposed, because of the fact that SAR images have much texture and SSIM algorithm has good performance in optical image quality assessment. TSSIM combines the image gray intensity with textural features to measure the image structural information. The Gray Level Co-occurrence Matrix (GLCM) is adopted to extract the textural features of SAR image. The angle second moment feature is proved the best compared with the other textural features. The results of simulations have shown that TSSIM is effective and can assess the jamming effect more accurately in different regions such as complex texture and simple texture area compared with SSIM and PSNR.