Synthetic aperture radar image target identification method based on depth model

A technology of synthetic aperture radar and depth model, which is applied in character and pattern recognition, instruments, computer components, etc., to achieve the effect of reducing costs

Active Publication Date: 2017-02-15
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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At the same time, the depth model has good parallel processing ability and learning ability, which can deal with the complex...

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  • Synthetic aperture radar image target identification method based on depth model
  • Synthetic aperture radar image target identification method based on depth model
  • Synthetic aperture radar image target identification method based on depth model

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[0048] In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the implementation methods and accompanying drawings.

[0049] use as figure 1 The 4-layer depth model structure shown realizes the present invention, wherein the first to third layers respectively include convolution filters, modified linear units and pooling filters, and the fourth layer is a fully connected layer, which includes convolution filters and modified linear unit. The input of the deep model is training samples and test samples. The convolution filters of the first to third layers use the sliding window method with the preset step size s=1 to input data (such as figure 2 ‐a) to perform convolution to obtain the convolution output, such as figure 2 As shown in -b; the pooling filter performs dimensionality reduction processing on the convolution output: the local maximum val...

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Abstract

The invention discloses a synthetic aperture radar image target identification method based on a depth model. The method comprises steps of image cutting; depth model level structure design, characteristic extraction filter design, parameter quantity control and overfitting prevention, function activation and non-linear lifting, identification classification and autonomous parameter correction update; depth model training; target identification. The method is advantaged in that filter parameters can realize autonomous iteration update in a training process, characteristic selection and extraction cost is greatly reduced, moreover, target different-level characteristics can be extracted through the depth model, the characteristics can be acquired through high-degree matching and training, so high-degree target representation can be realized, and target identification accuracy of SAR images is improved.

Description

technical field [0001] The invention relates to machine learning and deep learning application technology, in particular to the application of deep learning methods in synthetic aperture radar image target recognition. Background technique [0002] Synthetic Aperture Radar (SAR for short) can provide high-resolution images all day long under all weather conditions. The main method of the current SAR image target recognition system is to use the characteristics of the image target to train the classifier, and realize the SAR image target recognition through the classifier, so the performance of the classifier determines the recognition ability of the recognition system. [0003] The selection and extraction of features has a great influence on the design and performance of classifiers. Pattern recognition is the process of classifying specific things into a specific category, that is, first use a certain number of samples, design a classifier according to the similarity betw...

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Application Information

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IPC IPC(8): G06K9/46G06K9/62
CPCG06V10/443G06V10/462G06F18/2411
Inventor 曹宗杰肖蒙崔宗勇皮亦鸣闵锐李晋
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA
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