Synthetic Aperture Radar(SAR) imaging technique processes high-resolution images of Earth’s Surface irrespective of weather conditions. SAR images require large bandwidths to transmit to ground stations and received data acquires huge volumes of data. To overcome this challenge in this work, the sparsity nature of the SAR image is explored in the Fourier domain, and the complete signal is reconstructed using the fast Sparsity-independent regularized Pursuit (SIRP) reconstruction algorithm. The SIRP algorithm is suitably derived for SAR image reconstruction. It improves SAR image recovery compared to traditional CS recovery algorithms as it uses an optimized regularization strategy and does not depend on Sparsity. It also lessens the computational load using parallel estimation, which is crucial for quickly handling extensive amounts of SAR data. The proposed work is validated for the ERS-2 dataset, which is of size 4912 \(\,\times \,\) 29750. SIRP proves its improvement in reconstruction by showing a significant improvement in PSNR with less computational time compared to OMP and CoSAMP thereby enhancing remote sensing proficiencies.