AIS data-assisted SAR image Rayleigh CFAR detection algorithm

Abstract

The invention discloses an AIS data-assisted SAR image Rayleigh CFAR detection algorithm which comprises the steps of 1, acquiring an SAR image, setting a local sliding window consisting of a target window and a background window, automatically exporting ship distribution density in the local background window through AIS target distribution information data matched with the SAR image, and calculating adaptive truncation depth, then calculating Rayleigh statistical model distribution parameters of pixels in the background window to obtain a truncation rule, and finally removing heterogeneous point pixels in the truncation rule; 2, performing Rayleigh statistical model distribution parameter estimation on the reserved real sea clutter by adopting a maximum likelihood estimation method; 3, modeling the real sea clutter gray scale probability density by adopting Rayleigh distribution; and 4, establishing a judgment rule according to a given detection false alarm rate, and performing target judgment on the detected pixel in the target window. According to the method, a relatively low false alarm rate can be kept while a relatively high ship target detection rate is obtained.

Description

technical field [0001] The invention relates to the technical field of SAR image target detection, in particular to a SAR image Rayleigh CFAR ship detection method based on AIS data in a multi-ship target sea state environment. Background technique [0002] Synthetic Aperture Radar (SAR) is a new technology in the development of radar, it is a high-resolution active imaging sensor. Using SAR remote sensing means, it is possible to realize multi-polarization, multi-band, and multi-angle observation of ground objects, and the obtained image feature information is rich, including amplitude, phase and polarization and other information. Due to the all-day and all-weather observation capabilities of SAR, target detection using SAR images has been highly valued in the field of marine remote sensing, and has gradually become a research hotspot in the current stage of marine applications of SAR images. [0003] Due to the imaging characteristics of SAR, the sea clutter in SAR image...

AI Technical Summary

Problems solved by technology

However, these methods usually rely on a fixed threshold for clutter truncation. If the fixed threshold is incorrectly selected, a large number of real sea clutter samples will be removed or all high-intensity outlier pixels cannot be eliminated, resulting in a decrease in the accuracy of parameter estimation.

In addition, the clutter truncation and parameter estimation process based on a fixed threshold requires a large number of iterative calculations, and the parameter estimation accuracy is poor and the efficiency is low

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