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Local low-rank approach for parameter extraction of ocean internal wave from SAR image

Parameter retrieval of ocean internal waves from synthetic aperture radar (SAR) images is an important issue of oceanography; however, the extraction precision is usually limited by the complex waveforms and the noise. To improve the performance of parameter extraction, traditional methods usually focus on removing the noise on SAR images by exploiting its statistical character, but the structure character of ocean internal waves does not draw enough attentions to improve the image quality for parameter extraction. This study presents a local low-rank approach to extract the parameters robustly by associating the noise removing and parameter extraction together. An optimisation model is first developed by associating the speckle suppression and the exploration of the structural prior of internal wave, where the local low-rank prior is utilised. Also then, a numerical algorithm based on alternating optimisation scheme is designed to solve the proposed model. The proposed approach not only improves the extraction precision, but is also robust to the non-ideal cases such as the deformed waveforms. In experiments, both simulations and real data are used to demonstrate the efficiency and robustness of the proposed method.