Image Quality vs Performance in Super-Resolution for SAR Ship classification

Synthetic Aperture Radar (SAR) images for ship classification often face the problem of low resolution. Techniques like super-resolution (SR) can help to enhance the images for better ship classification. In this paper, we compared traditional interpolation techniques (bilinear, bicubic, Lanczos, nearest-neighbor) with deep learning SR methods (EDSR, RCAN, CARN) at 2x and 4x resolutions to analyze their effect in terms of image quality and classification performance. The image quality was assessed using metrics like Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM). The findings indicate that while 2x resolution images typically achieved higher image quality scores, the 4x images often performed equally well or better in classification tasks. We utilized two versions of VGG: SR techniques yielded similar scores with a simple VGG, whereas, in the multi-scale VGG (MSVGG), traditional interpolation methods outperformed deep learning methods. Experiments confirm that super-resolved images reach high scores in terms of classical image quality metrics. However, this does not always translate directly into improved performance in SAR ship classification. This highlights the need to select SR techniques by jointly evaluating image quality metrics and classification performance.