SAR target identification method based on Bayes multinuclear learning support vector machine

Abstract

The invention discloses an SAR target identification method based on a Bayes multinuclear learning support vector machine. The objective of the invention is to solve a problem of inaccurate SAR image target identification in the current target identification method. The method comprises following steps of 1) inputting an original SAR image and carrying out preprocessing to calculate nuclear matrixes of different characteristics; 2) according to the multinuclear learning method, combining the nuclear matrixes; 3) establishing a Bayes multinuclear learning support vector machine model for a support vector machine according to the combined nuclear matrixes; 4) using the expectation maximization algorithm to solve the Bayes multinuclear learning support vector machine model to obtain an optimal solution; and 5) using the optimal solution to carry out target identification on SAR image test data. According to the invention, by effectively combining the deduction capability of the Bayes method and the distinguishing capability of the multinuclear learning method, the identification performance is improved and the method can be used for classification of SAR images.

Description

technical field [0001] The invention belongs to the technical field of radar target recognition, in particular to a SAR target recognition method, which can be used for the classification of SAR images. Background technique [0002] Synthetic Aperture Radar (SAR) is an active sensor that uses microwaves for perception. Its imaging is not affected by objective factors such as light and climate, and it can monitor targets all day and all day. High utility value. In addition to the target, the SAR image also contains a large number of clutter, and the SAR image also contains a large number of coherent spots, which makes the detection, identification and recognition of the SAR image very difficult; in addition, due to the different configurations of the SAR target Due to the complexity of the environment, it is impossible to obtain training samples in all situations. Therefore, how to improve the performance of SAR target recognition is an important research direction in radar...

AI Technical Summary

Problems solved by technology

[0009] This kind of classifier that combines single data features with support vector machines is called single-core learning support vector machines. Because different data features have different abilities to represent the similarity and differentiation of data, different data features are selected, and single-core learning The support vector machine shows completely different classification performance. Therefore, the single-core learning support vector machine can only show the characteristics of a certain data feature, and cannot reflect the correlation between the data features, thus affecting the classification performance of the classifier. Make the target recognition rate drop

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