Deep neural network-based SAR texture image classification method

A deep neural network and neural network technology, applied in the field of SAR texture image classification, to achieve the effect of improving classification accuracy, improving efficiency, high robustness and classification accuracy

Active Publication Date: 2015-03-11
XIDIAN UNIV
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Problems solved by technology

However, traditional machine learning and signal processing methods are s...

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  • Deep neural network-based SAR texture image classification method
  • Deep neural network-based SAR texture image classification method
  • Deep neural network-based SAR texture image classification method

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Embodiment Construction

[0036] Reference figure 1 , The implementation steps of the present invention are detailed as follows:

[0037] Step 1. Define a deep neural network composed of three layers.

[0038] Such as figure 2 As shown, the deep neural network defined in this example includes a three-layer structure, in which the first layer and the third layer are both a radial basis function RBF neural network composed of an input unit, a hidden unit and an output unit; the second The layer is a restricted Boltzmann machine RBM neural network composed of a hidden unit and a visual unit.

[0039] Step 2: Train the deep neural network by learning the texture classification features of the SAR image training samples.

[0040] (2a) Extract texel features and grayscale features of SAR image training samples, that is, low-level features of SAR image training samples;

[0041] Select the SAR image containing three types of features of town, farmland, and mountains from the SAR image feature database as the first ex...

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Abstract

The invention discloses a deep neural network-based SAR (Synthetic Aperture Radar) texture image classification method, and aims to mainly solve the problem of low accuracy of SAR texture image classification with a larger number of samples and more characteristic dimensions in the prior art. The method is implemented by the following steps: (1) extracting low-level characteristics of an SAR image; (2) training the low-level characteristics of the SAR image to obtain advanced characteristics of the image by virtue of a first layer of RBF (Radial Basis Function) neural network of a deep neural network; (3) training the advanced characteristics to obtain more advanced characteristics of the image by virtue of a second layer of RBM (Restricted Boltzmann Machine) neural network of the deep neural network; (4) training the more advanced characteristics to obtain image texture classification characteristics by virtue of a third layer of RBF neural network of the deep neural network; (5) comparing texture classification characteristics of an image test sample with a test sample tag, and regulating parameters of each layer of the deep neural network to obtain the optimal test classification accuracy. The method is high in classification accuracy, and can be used for target identification or target tracking.

Description

Technical field [0001] The invention belongs to the technical field of image processing, and particularly relates to a deep neural network-based multi-sample, multi-category, and complex feature SAR texture image classification method, which can be used in the fields of target recognition, target tracking and the like. Background technique [0002] Synthetic aperture radar SAR is widely used in the field of earth science remote sensing. SAR texture image classification is the application of pattern recognition in SAR image processing. It converts image data from two-dimensional gray space to target pattern space. The result of the classification is to divide the image into multiple different categories according to different attributes Sub-area. The reliable classification features of SAR images are mainly gray-scale features and texture features, but the results obtained by using gray-scale features for classification in practical applications are not very ideal, so good textur...

Claims

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Application Information

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IPC IPC(8): G06K9/66G06K9/46G06N3/02
CPCG06N3/088G06F18/2413
Inventor 焦李成李玲玲韩佳敏屈嵘杨淑媛侯彪王爽刘红英熊涛马文萍马晶晶
Owner XIDIAN UNIV
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