Comparative Study of Adaptive Filters, Boxcar, and Goldstein in Radar Interferometry Using Envisat Satellite Images over the Yazd-Ardakan Plain

 This study aims to evaluate and identify the most effective speckle noise reduction filter for Synthetic Aperture Radar (SAR) images, specifically focusing on maintaining image clarity and preserving edge details.

  SAR systems, which send microwave pulses to measure reflectors and distances, are prone to speckle noise due to the interference of reflected waves from heterogeneous surfaces. This noise negatively impacts image quality, complicating the extraction of accurate environmental data. The research utilized 1-Sentinel SAR images of the Yazd-Ardakan plain and applied various noise reduction filters to these images.

 The effectiveness of each filter was assessed based on its ability to reduce speckle noise while preserving critical features, particularly edges. Results indicated that the adaptive filter outperformed the other filters by maintaining sharp edges and reducing noise, making it the most suitable option for this type of image enhancement. The Goldstein filter, while effective in removing noise, compromised phase accuracy, and the Boxcar filter blurred edges.

This study concludes that the adaptive filter is the best option for SAR image enhancement in the study area, offering significant potential for more accurate environmental monitoring. Further research is recommended to explore the combination of different filters for improved results in complex environments.