SAR automatic object recognition method based on heterogeneous CNN (Convolutional Neural Network) integration

Abstract

The invention provides an SAR automatic object recognition method based on heterogeneous CNN (Convolutional Neural Network) integration, and relates to the field of radar target recognition. The method employs a heterogeneous CNN, achieves the full extraction of the target feature information in an SAR image, and achieves the target class prediction. Based on the integrated learning theory, the method integrates the results of a plurality of CNNs, obtains a final class label, and achieves the quick and precise recognition of an SAR target. According to the invention, the method can achieve theadjustment of the number of heterogeneous networks according to the actual hardware conditions and a performance index, still can guarantee the higher recognition rate under the condition that the number of training samples is smaller, is high in efficiency, is stable, and is strong in generalization capability.

Description

technical field [0001] The invention belongs to the field of radar target recognition, in particular to a SAR automatic target recognition method based on heterogeneous convolutional neural network integration. Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution microwave imaging radar with all-weather and all-weather working capabilities. It is widely used in military reconnaissance, earth remote sensing, disaster prediction and other fields. value. Because the SAR image reflects the electromagnetic scattering characteristics and geometric structure characteristics of the target within its observation range, the target characteristics are quite different from the optical image; in addition, due to the influence of the observation environment and the coherent imaging mechanism, there are a large number of coherent spots in the SAR image, which makes The difference between it and the optical image is further increased, which increases the diffic...

AI Technical Summary

Problems solved by technology

[0004] In the existing technology, there is a problem that the feature extraction ability of a single network is limited, and its classification performance is easily affected by the training parameters, and the network generalization ability is poor; and most of the networks used in the existing neural network integration methods are ordinary feedforward neural networks. The network has the same structure, it is not suitable for the extraction of two-dimensional features in the image, and it cannot fully mine the target features contained in the SAR image

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