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A squint dual-antenna interferometric SAR (AT-InSAR) ocean current measurement method based on the traditional cross-track InSAR is studied in this paper. In the imaging geometry of cross-track InSAR, we can get an along-track baseline component in the presence of squint, which can be used for ocean current measurement. This additional working mode can extend the application efficiency of DEM acquisition InSAR satellites when flying over the ocean. The imaging geometry of dual-antenna squint interferometric SAR is established, and ocean current velocity measurement principle is given. Mainly, the ocean current velocity measurement performance of this novel working mode is analyzed.

In this paper, the ship target detection and recognition system of remote sensing imaging based on machine learning is designed according to the sparsity of interest targets in optical remote sensing image, and proposes a method of target detection and recognition based on morphological matching and machine learning. The slices of suspected targets are extracted quickly by visual enhancement technology that the amount of data processed is greatly reduced. The target information of interest is extracted in depth and the false alarm rate of detection is greatly reduced by using machine learning method to classify objects. In the system function and performance verification test, the real-time and accuracy index through 227 targets of 32 scenes GF-2 satellite images are tested what can detect and recognize about 20 objects per second. The recall rate of the system is more than 92%, and the efficiency of target detection method based on traditional morphological matching is less than 60% while the target recognition method base on machine learning improves the precision rate to over 97%.