SAR Image Formation Method with Azimuth Periodically Missing Data Based on RELAX Algorithm.

Area of Science:

• Remote Sensing
• Signal Processing
• Synthetic Aperture Radar (SAR) Imaging

Background:

• Synthetic Aperture Radar (SAR) is a crucial remote sensing technique.
• Data acquisition in SAR can result in the loss of raw echo data, particularly along the azimuth.
• Periodically missing data in SAR echo signals leads to artificial artifact targets, complicating image formation.

Purpose of the Study:

• To analyze the imaging challenges posed by periodically missing azimuth data in SAR echo signals.
• To propose a novel imaging method capable of recovering lost SAR data and producing well-focused images.
• To validate the proposed method's effectiveness using both simulated and real SAR data.

Main Methods:

• Development of a mathematical model to describe artifacts caused by periodically missing data.
• Application of the RELAX algorithm, adapted for periodic notch data, for echo signal recovery.
• Implementation of block restoration along the azimuth for efficient processing of large 2D echo data.

Main Results:

• Successful recovery of SAR echo data and generation of well-focused images even with a 50% azimuth data loss rate.
• Demonstration of the algorithm's advantages through processing of simulated point target and real raw SAR echo data.
• Quantitative validation using image entropy and Structural Similarity Index (SSIM) to prove superiority over existing methods.

Conclusions:

• The proposed RELAX algorithm-based method effectively addresses the problem of periodically missing SAR azimuth data.
• The technique enables high-quality SAR image formation despite significant data loss.
• This advancement offers a robust solution for improving SAR imaging reliability in challenging data acquisition scenarios.