Polarized sar ship detection method based on k-wishart distribution

Abstract

The invention discloses a polarized SAR ship detection method based on K-WISHART distribution. The polarized SAR ship detection method mainly comprises following steps of: successively acquiring a polarized SAR radar echo sample and a covariance matrix C of the polarized SAR radar echo sample, and setting T as the number of the classification of the C; successively calculating posterior probabilities of n samples in the covariance matrix of a T-type polarized SAR radar echo sample, and a covariance matrix C of the mth-type polarized SAR radar echo sample and then calculating actual level values corresponding to polarized SAR radar echo samples of F channels; if each of the actual level values is larger than or equal to a set significance level, determining that the classification result of Cm is correct; then calculating SPAN average values of covariance matrixes of the T-type polarized SAR radar echo sample respectively, and obtaining a biggest SPAV average value in the T SPAN average values; and taking the type to which the biggest SPAN average value belongs to be a ship, and the rest T-1 type as the sea level.

Description

technical field [0001] The invention belongs to the technical field of radar signal processing, in particular to a polarization SAR ship detection method based on K-WISHART distribution, which is suitable for high-resolution detection of sea-level ships. Background technique [0002] Synthetic Aperture Radar (SAR) has a wide range of uses and can work all day and all day without considering the influence of light and weather, so it plays an important role in ship detection. In the SAR image, due to the structure of the ship itself and the large radar cross-section, the ship has a relatively large backscattering energy relative to the sea level. If the statistical distribution of sea level is known, the problem of selecting the detector threshold can be solved by constraining the detection probability or false alarm probability level. However, the probability density function (PDF) derivation of the ship is very complicated, and the constant false alarm probability (CFAR) is...

AI Technical Summary

Problems solved by technology

[0005] The ship detection methods mentioned above are all proposed under the condition that sea level clutter is based on Gaussian distribution, but the statistical characteristics of sea level do not always present Gaussian distribution, especially in high resolution and wind-induced clutter. Ship detection at sea level is more difficult in high sea conditions

Images