CS Imaging Algorithm for RADARSAT Raw Data and Synthetic Aperture Radar

This article discusses the CS imaging algorithm for RADARSAT raw data and Synthetic Aperture Radar technology. These algorithms and radar technologies represent crucial components in modern remote sensing, widely applied in earth observation, environmental monitoring, and resource surveys. The CS (Compressed Sensing) imaging algorithm leverages compressed sensing theory to efficiently reconstruct high-quality images from raw data, implementing sparse signal recovery through optimization techniques like L1-norm minimization. Key implementation aspects include designing measurement matrices, applying sparse transforms (such as wavelet or Fourier transforms), and using reconstruction algorithms like Orthogonal Matching Pursuit (OMP) or Basis Pursuit. Synthetic Aperture Radar technology employs synthetic aperture principles to create large virtual apertures, achieving higher resolution and detailed terrain information through precise phase compensation and motion compensation algorithms. The development and research of these technologies significantly enhance the quality and accuracy of remote sensing imagery, with practical implementations involving Doppler parameter estimation, azimuth compression, and range migration correction in SAR processing chains.