SAR target identification method based on ensemble learning

Abstract

The invention relates to a SAR target identification method based on ensemble learning and mainly solves a problem of low SAR target identification performance because of imbalance training data class distribution in the prior art. The method comprises steps that 1, word package model characteristics of a given training slice and a test slice are extracted; 2, random downsampling of a clutter training sample is carried out to acquire a sub set, the acquired sub set and a target type training sample are trained together to acquire a cost-sensitive dictionary; 3, random downsampling of the clutter training sample is carried out to acquire the sub set, the acquired sub set and the target type training sample are trained together to acquire a SVM classifier; 4, the cost-sensitive dictionary and the SVM classifier are utilized to classify the test sample, and a classification decision value of the test sample is recorded; and 5, a maximum voting method is utilized to determine the classification decision value of the test sample to determine a final class label of the test sample. The method is advantaged in that identification performance is improved, and the method can be applied to SAR target identification in a complex scene during imbalance training data class distribution.

Description

technical field [0001] The invention belongs to the technical field of radar, in particular to a SAR target identification method, which can be used to provide important information for vehicle target identification and classification. Background technique [0002] Synthetic aperture radar SAR uses microwave remote sensing technology, is not affected by climate and day and night, has all-weather and all-weather working capabilities, and has the characteristics of multi-band, multi-polarization, variable viewing angle and penetrability. With the emergence of more and more airborne and spaceborne SARs, a large number of SAR data in different scenarios are brought. An important application of SAR data is automatic target recognition (ATR). Target identification in complex scenes has also become a current research direction. one. [0003] SAR target discrimination refers to a classifier learned from the training data set, which can be used to predict the category label of unkno...

AI Technical Summary

Problems solved by technology

[0005] 1. Since the traditional classification method basically takes the maximum overall classification accuracy of the training data as the classification criterion, when the distribution of training sample categories is unbalanced, the target class data accounts for a small proportion in the training data set, and the classifier usually tends to use the target class The class is judged as clutter class, so the detection rate of target class samples is low, which leads to poor performance of SAR target identification

[0006] 2. Because in the process of SAR target identification, more attention is usually paid to the accuracy of the target class. When the distribution of training sample categories is unbalanced, the classifier trained by the traditional classification method will produce a high detection rate for clutter samples. However, the detection rate of the target class samples is very low. When facing the SAR target identification of the unbalanced data set, it is necessary not only to maintain the original classification accuracy of the clutter class, but also to greatly improve the classification accuracy of the target class. Therefore, the existing These traditional SAR target identification methods cannot meet this requirement

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