A Synthetic Aperture Radar Target Recognition Method Based on Auxiliary Decision Update Learning

Abstract

The invention belongs to the technical field of radar remote sensing applications, and in particular relates to a synthetic aperture radar target recognition method based on auxiliary decision update learning. The method of the present invention uses a small amount of initial training samples to train the initial model, the newly added unlabeled image is used as the test sample, and the recognition result is used as the training sample for the next training, and iteratively trains on the basis of the existing model until a recognition efficiency is obtained. Stable and mature identification system. The invention uses the convolutional neural network as the main body to extract the deep features of the SAR target for classification, and then combines the auxiliary judgment of the auxiliary classifier, so that the newly added unlabeled SAR image can be directly applied to the existing classifier, and at the same time, repeated training of samples is avoided. , improving the recognition efficiency.

Description

[0001] technical field [0002] The invention belongs to the technical field of radar remote sensing applications, and in particular relates to a synthetic aperture radar target recognition method based on auxiliary decision update learning. Background technique [0003] Synthetic Aperture Radar (hereinafter referred to as SAR) has the characteristics of all-time and all-weather, and is an important means of earth observation. SAR target recognition uses SAR image information to realize the determination of target types, models and other attributes. It has clear application requirements in military fields such as battlefield reconnaissance and precision strikes. It is one of the key technologies to improve the information perception ability of SAR sensors and realize the application of SAR technology. [0004] SAR target recognition performance is closely related to training samples. Target recognition requires a large number of samples with classification labels, which requi...

AI Technical Summary

Problems solved by technology

However, these studies are based on optical image data

However, the imaging mechanism of SAR images is very different from that of ordinary optical sensors, resulting in that SAR images cannot be intuitively understood like optical images. The newly added SAR images do not have classification labels, and it must be trained to confirm the radar images conveyed. information, and complete manual reading and understanding cannot meet the real-time requirements of some applications

At the same time, compared with optical images, the special imaging mechanism of SAR images causes certain distortions in SAR images, which makes feature extraction for SAR images difficult.

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