A SAR image target feature extraction and recognition method based on kfda and svm

Abstract

The present invention provides a SAR image target feature extraction and recognition method based on KFDA (Kernel Fisher Discriminant Analysis) and SVM (Support Vector Machine), comprising the following steps: performing training target samples of known categories and testing target samples of unknown categories Amplitude data normalization processing; using the KFDA criterion to perform feature extraction on the normalized known categories of training target samples and unknown categories of test target samples; The SVM classifier is trained to generate the optimal classification surface; finally, the features of the unknown category of test target samples extracted by the KFDA criterion are identified through the optimal classification surface; the present invention reduces the requirements for the preprocessing process and overcomes the The azimuth sensitivity of SAR images compresses the dimensionality of sample features and obtains a high target recognition rate, which has good generalization.

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...

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Problems solved by technology

[0004] In 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 the 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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