SAR (Synthetic Aperture Radar) image target characteristic extraction and identification method based on KFDA (Kernel Fisher Discriminant Analysis) and SVM (Support Vector Machine)

Abstract

The invention provides an SAR (Synthetic Aperture Radar) image target characteristic extraction and identification method based on KFDA (Kernel Fisher Discriminant Analysis) and an SVM (Support Vector Machine). The method comprises the following steps: performing amplitude data normalization processing on a training target sample of a known type and a testing target sample of an unknown type; performing characteristic extraction on the normalized training target sample of the known type and the testing target sample of the unknown type respectively by using a KFDA criterion; training an SVM classifier by using training target sample characteristics of known types extracted according to the KFDA criterion to generate an optimal classification face; identifying the characteristics of the testing target sample of the unknown type extracted according to the KFDA criterion through the optimal classification face. By adopting the method, the requirement on a preprocessing process is lowered, the target-aspect sensitivity of an SAR image is avoided, the dimensions of sample characteristics are compressed, and high target identification rate is obtained. The method has high popularity.

Description

technical field [0001] The invention belongs to the field of SAR image processing and pattern recognition, and relates to a SAR image target feature extraction and recognition method based on KFDA (Kernel Fisher Discriminant Analysis) and SVM (Support Vector Machine). Background technique [0002] Synthetic Aperture Radar (SAR) is an active sensor using microwave perception. It can conduct all-weather and all-weather reconnaissance on targets or areas of interest. Penetration ability of ground objects. The so-called radar target recognition is based on the detection and positioning of the target by radar, and according to the radar echo signal of the target and the environment, the target features are extracted to realize the determination of the attribute, category or model of the target. With the continuous maturity of SAR imaging technology, target recognition based on SAR images has become more and more important. [0003] In the target recognition process based on SAR...

AI Technical Summary

Problems solved by technology

[0004] Among the target feature extraction methods of SAR images, the most commonly used methods are principal component analysis (PCA), kernel function principal component analysis (KPCA) and other methods. The disadvantage of the principal component analysis method is that it cannot extract the nonlinear features existing in the image. , and the disadvantage of the principal component analysis method of kernel function is that the extracted features do not have good class discrimination ability and the feature dimension is high; in the target recognition method of SAR image, the most commonly used is the maximum correlation classifier and the most recent The disadvantage of the maximum correlation classifier is that when the sample dimension is high, the algorithm complexity is also high, and the disadvantage of the nearest neighbor classifier is that the selected optimal classification surface is not the global optimal

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