SAR target identification method based on multi-level features

Abstract

The invention discloses an SAR target recognition method based on multi-level features, which comprises the following steps of: firstly, establishing pattern expression of SAR target image features, performing feature extraction to obtain feature vectors and a feature saliency map of a target, and fusing the extracted feature vectors to obtain shallow features; connecting an original target image and the extracted image feature saliency map according to a channel to obtain an input image, extracting features of the input image by using a deep convolutional network, extracting an output of a middle convolutional layer as a middle-layer feature, and extracting an output of a final full-connection layer as a deep-layer feature; performing further adaptive weight fusion on the obtained features of the shallow layer, the middle layer and the deep layer; and finally, carrying out classification identification on the fused features by utilizing a trained machine learning classification model to obtain a final identification result. According to the method, the respective advantages of the shallow, middle and deep features are combined, the synergistic effect of the shallow, middle and deep features is brought into full play, and the utilization capability of the multi-level features and the precision of target recognition are improved.

Description

technical field [0001] The invention belongs to the application field of radar target recognition, and in particular relates to a multi-level feature-based SAR target recognition method. Background technique [0002] SAR (Synthetic Aperture Radar) has the characteristics of all-day, all-weather, high resolution, large width, and has a certain ability to penetrate the ground, so it is extremely valuable in the military field. SAR image target recognition has been a research hotspot in the field of SAR image interpretation. The traditional SAR image target recognition method is mainly composed of independent steps such as preprocessing, feature extraction, recognition and classification. The feature extraction process generally requires the use of SIFT, HOG, PCA, LDA and other algorithms to extract well-discriminative features in order to achieve better classification, while the classifier uses SVM (Support Vector Machine), AdaBoost (Adaptive Boosting), SRC (Sparse Represent...

AI Technical Summary

Problems solved by technology

[0005] Purpose of the invention: In order to solve the existing problems and deficiencies in the prior art, the present invention provides a SAR target recognition method based on multi-level features to solve the problems of low accuracy and poor stability of SAR target recognition

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