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Residual convolutional neural network and PCA dimensionality reduction fused SAR automatic target recognition method

A technology of convolutional neural network and automatic target recognition, which is applied in neural learning methods, biological neural network models, character and pattern recognition, etc., can solve the problems of low recognition accuracy and achieve high recognition rate, good recognition effect, and automation high degree of effect

Inactive Publication Date: 2018-11-30
ZHEJIANG UNIV
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Problems solved by technology

[0005] In order to overcome the deficiencies of current SAR automatic target recognition technology, the purpose of the present invention is to provide a kind of SAR automatic target recognition method that fuses residual convolutional neural network and PCA dimensionality reduction, mainly solves the problem that the recognition accuracy rate of prior art is lower

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  • Residual convolutional neural network and PCA dimensionality reduction fused SAR automatic target recognition method
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  • Residual convolutional neural network and PCA dimensionality reduction fused SAR automatic target recognition method

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Embodiment Construction

[0053] The present invention will be described in detail below according to the accompanying drawings.

[0054] refer to figure 1 , the specific implementation steps of the present invention are as follows:

[0055] 1) Obtain the SAR target image data and mark it according to the target category to form a training set.

[0056] For the multiple types of targets involved in the target recognition task, several SAR images are collected for each type as training samples.

[0057] 2) Perform data enhancement and preprocessing on the given training set images.

[0058] 2.1) Data enhancement:

[0059] Perform random translation, flip, rotation and scaling operations on each SAR image in the training set, and generate derived training samples with the same label as the original training samples;

[0060] 2.2) Preprocessing:

[0061] 2.2.1) Use the filtering algorithm to process the SAR image to suppress coherent speckle noise. The pixel value after image filtering is calculated ...

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Abstract

The invention discloses a residual convolutional neural network and PCA dimensionality reduction fused SAR automatic target recognition method. The method includes: acquiring an SAR target image dataand labeling a target category of the SAR target image data to form a training set; performing enhancement and expansion and pre-processing on training set image data, and constructing a residual convolutional neural network; inputting training samples into the network to perform training; inputting the training samples into the trained network model, and using feature vectors obtained after the samples pass all the hidden layers as a new training set; using the PCA dimensionality reduction method to perform dimensionality reduction on the obtained feature vectors, and then inputting the feature vectors into an SVM classifier to perform training; and finally pre-processing samples to be recognized, inputting the pre-processed samples to be recognized, obtaining feature vectors, performingPCA dimensionality reduction on the feature vectors, and using the trained SVM classifier to perform recognition. The method solves the problem that the existing SAR automatic target recognition technology has low recognition accuracy.

Description

technical field [0001] The invention belongs to the technical field of radar data processing, in particular to a SAR automatic target recognition method, which mainly solves the problem of low recognition accuracy in the prior art. Background technique [0002] Synthetic Aperture Radar (SAR) is an active high-resolution imaging radar, which can obtain images similar to visible light images under the weather conditions of cloud cover and low visibility. SAR has strong penetrating power, high resolution, and large-scale monitoring capabilities. These advantages make it have significant advantages in applications such as environmental monitoring, ocean monitoring, map mapping, and military reconnaissance. Therefore, the research on SAR radar-related technologies has attracted more and more attention from all over the world, and SAR image automatic target recognition is one of the most important technologies. [0003] Xidian University proposed a CNN-based SAR target recognitio...

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Application Information

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IPC IPC(8): G06K9/00G06K9/40G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06V10/30G06V2201/07G06N3/045G06F18/2135G06F18/2411G06F18/214
Inventor 万子宁刘兴高
Owner ZHEJIANG UNIV
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