SAR image target identification method based on multi-discriminator generative adversarial network

Abstract

The invention relates to an SAR image target recognition method based on a multi-discriminator generative adversarial network, and the method comprises the steps: obtaining an original real sample, and generating a training sample data set based on a multi-discriminator generative adversarial network model; training the convolutional neural network by using the training sample data set to obtain atrained target recognition model; and inputting a to-be-detected SAR image to the target recognition model, and outputting a target recognition result corresponding to the to-be-detected SAR image through feature extraction and feature matching. Compared with the prior art, the invention provides a multi-discriminator generative adversarial network model for generating a high-quality and high-stability training sample data set. A training mode of multi-discriminator joint feedback is adopted, a dynamic adjustment selection function is utilized to fuse output results of discriminators to complete update training of generators, training stability and generated sample quality are ensured, the reliability of subsequent target recognition model training is further improved, and accuracy of SARimage target recognition is improved.

Description

technical field [0001] The invention relates to the technical field of SAR image target recognition, in particular to a SAR image target recognition method based on multi-discriminator generation confrontation network. Background technique [0002] SAR images are generated by the SAR (Synthetic Aperture Radar) system. Since SAR is an advanced active microwave earth observation equipment, it has a certain penetration ability, can obtain images similar to optical photos, and effectively detect camouflage Therefore, SAR image target recognition is of great significance to the national economy and military applications. [0003] At present, the target recognition of SAR images is mostly based on deep learning related technologies, which have high requirements for the quantity and quality of training data. However, the existing SAR target image samples are limited in number and difficult to obtain, making it difficult for the target recognition model to obtain sufficient train. ...

AI Technical Summary

Problems solved by technology

[0003] At present, the target recognition of SAR images is mostly based on deep learning related technologies, which have high requirements for the quantity and quality of training data. However, the existing SAR target image samples are limited in number and difficult to obtain, making it difficult for the target recognition model to obtain sufficient train

The Generative Adversarial Networks (GAN) technology proposed in recent years is one of the most effective data expansion algorithms. Applying GAN technology to the data expansion of SAR image training samples can increase the number of training data to a certain extent. However, the traditional GAN ​​model training has poor robustness, resulting in unstable quality of generated training data, which affects the training of subsequent target recognition models, and cannot guarantee the accuracy of SAR image target recognition.

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