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Synthetic Aperture Radar (SAR) is active and coherent microwave high resolution imaging system, which has the capability to image in all weather and day-night conditions. SAR transmits chirp signals and the received echoes are sampled into In-phase (I) and Quadrature (Q) components, generally referred to as raw SAR data. The various modes of SAR coupled with the high resolution and wide swath requirements result in a huge amount of data, which will easily exceed the on-board storage and downlink bandwidth of a satellite. This paper addresses the compression of the raw SAR data by sampling the signal below Nyquist rate using ideas from Compressed Sensing (CS). Due to the low computational resources available onboard satellite, the idea is to use a simple encoder, with a 2D FFT and a random sampler. Decoding is then based on convex optimization or uses greedy algorithms such as Orthogonal Matching Pursuit (OMP).