We propose a moving target analysis algorithm using speeded-up robust features (SURF) and regular moment in inverse synthetic aperture radar (ISAR) image sequences. In our study, we first extract interest points from ISAR image sequences by SURF. Different from traditional feature point extraction methods, SURF-based feature points are invariant to scattering intensity, target rotation, and image size. Then, we employ a bilateral feature registering model to match these feature points. The feature registering scheme can not only search the isotropic feature points to link the image sequences but also reduce the error matching pairs. After that, the target centroid is detected by regular moment. Consequently, a cost function based on correlation coefficient is adopted to analyze the motion information. Experimental results based on simulated and real data validate the effectiveness and practicability of the proposed method.
For better using of inverse synthetic aperture radar (ISAR) images of ship targets, it is more desirable to select a proper imaging time to obtain high quality top-view or side-view images. However, optimum imaging time selection is not robust enough for the restriction of traditional geometric feature extraction methods. In our study, we propose a method based on the geometric features and gradient maximization. First, we select the imaging instant from radar echoes by the centerline and mainmast of the ship. In this part, we propose a geometric features extraction method to improve the robustness of instant selection in different scenarios. Then, an image gradient maximization is employed to estimate the period for ISAR imaging. Finally, experimental results of both simulated and real signals are provided to demonstrate the effectiveness and practicability of the algorithm.
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