Observing Ocean Front by Retrieving Doppler Anomaly from GaoFen-3 SAR Images

Ocean front, internal wave and ocean vortex are general marine physical phenomena. The traditional observation method is from the angle of image gray level, even the existing deep learning network is based on image gray level through line detection. In this paper, a new observation method is proposed, that is, the retrieved Doppler anomaly of ocean wave motion relative to satellite antenna. For estimating Doppler anomaly, a new algorithm is proposed, which is based on Bayesian estimation method and reaches Cramer boundary through iteration. To verify the effectiveness of the algorithm, this paper uses GaoFen-3 SLC (single look complex image) SAR image. The results of local radial velocity distribution of inversion results are analyzed. The gradient distribution of local radial velocity, that is, the place where the velocity of ocean front changes the most, has the largest change in velocity gradient, which can better explain the wave modulation effect in ocean physics. Compared with the conventional method, our method can better understand and explain the marine physical phenomena by retrieving the radial velocity of ocean current.