A robust framework for removing G0 distributed noise from Synthetic Aperture Radar images

Abstract

Synthetic Aperture Radar (SAR) imagery finds widespread applications across engineering disciplines, encompassing vegetation surveys, biomass estimation, and weather-related investigations. However, two significant challenges hinder SAR image analysis: the inherent difficulty in visually interpreting SAR images and the adverse impact of multiplicative speckle noise. The proposed study introduces a robust framework for removing G 0 distributed noise from Synthetic Aperture Radar (SAR) images. This framework utilizes Bitonic preprocessing filters and SailFish optimization within the wavelet domain to address challenges in SAR image analysis, particularly the difficulty in visually interpreting SAR images and the impact of multiplicative speckle noise. By assessing noise distribution and applying denoising algorithms tailored to estimated noise distribution, the study overcomes limitations of linear filters by adopting Bitonic filters for effective preprocessing. Through parameter optimization using SailFish Optimization (SFO) and objective functions like Structural Similarity Index Measure (SSIM) and Edge Preservation Index (EPI), the proposed method outperforms conventional algorithms on both synthetic and real SAR data. Furthermore, for synthetic SAR data with noise modeled as G0 distributed, our approach provides a more realistic benchmark for comparison than existing methods.

Keywords

• Synthetic aperture radar
• G0 distribution
• bitonic filter
• wavelet transform
• non local means filter
• No-Reference quality evaluation