SAR automatic target recognition method based on variable convolutional neural network

An automatic target recognition, convolutional neural network technology, applied in neural learning methods, biological neural network models, scene recognition and other directions, can solve the problem of not much improvement in overall recognition performance, mining and utilization of SAR image target scattering characteristics and morphological features, etc. To achieve the effect of alleviating insufficient sample size, avoiding insufficient feature extraction, and strong generalization ability

Active Publication Date: 2020-12-18
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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Problems solved by technology

However, the scattering characteristics and morphological features of SAR image targets have not been fully exploited and utilized by the network, and the overall recognition performance has not been greatly improved.

Method used

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  • SAR automatic target recognition method based on variable convolutional neural network
  • SAR automatic target recognition method based on variable convolutional neural network
  • SAR automatic target recognition method based on variable convolutional neural network

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Embodiment Construction

[0024] In order to facilitate those skilled in the art to understand the technical content of the present invention, the content of the present invention will be further explained below in conjunction with the accompanying drawings.

[0025] A. Get the original SAR image:

[0026] Collect target images with the same resolution and their corresponding azimuth angle data, where the azimuth angles are distributed within the range of 0° to 360°.

[0027] B. Preprocessing the original SAR image:

[0028] B.1 First, for the acquired synthetic aperture radar target image, according to the azimuth angle of the SAR target acquired in step A, the original SAR image is rotated to the same direction by this angle. The mapping transformation between pixels satisfies formulas (1) and (2).

[0029]

[0030]

[0031] in, Represents the angle at which the image is rotated counterclockwise, x, y represent the abscissa and ordinate of the original image, respectively, x', y' represent ...

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Abstract

The invention discloses an SAR (Synthetic Aperture Radar) automatic target recognition method based on a variable convolutional neural network, which is applied to the field of radar target recognition and aims to solve the problems that scattering characteristics and morphological characteristics of an SAR image target in the prior art are not fully mined and utilized by a network and the overallidentification performance is not greatly improved. According to the invention, through data expansion and based on the idea of adding spatial sampling positions of extra offsets in the modules, sampling points of a convolution kernel in an input characteristic spectrum are deviated and concentrated in an interested area or target, the influence of different azimuth angles of an SAR target is overcome, the problem of insufficient SAR image sample size is alleviated, the condition of insufficient characteristic extraction is avoided, the generalization ability is high, the specific form and scattering information of the SAR target can be effectively utilized, and the target can be accurately identified and classified.

Description

technical field [0001] The invention belongs to the field of radar target recognition, in particular to a technology for automatically acquiring target information and categories. Background technique [0002] Synthetic Aperture Radar (SAR) is a high-resolution imaging radar, which can realize all-day and all-weather earth observation without limitation of light and climate conditions. Fields such as battlefield perception and reconnaissance, agricultural and forestry environmental monitoring, and geological and geomorphic exploration have broad application prospects, and have extremely high civilian and military value. SAR Automatic Target Recognition (ATR) is an image interpretation technology based on modern signal processing and pattern recognition theory. The included target categories provide strong support for battlefield intelligence analysis. [0003] At present, in the process of SAR ATR, the target is effectively recognized mainly through the template-based meth...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/13G06N3/045G06F18/2415
Inventor 裴季方黄钰林汪志勇霍伟博王陈炜张寅杨海光杨建宇张永伟
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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