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Method for sorting radar two-dimension image based on multi-dimension geometric analysis

A technology of geometric analysis and classification method, applied in the field of image processing, can solve problems such as the inability to reflect the validity of classification features and the inability to effectively represent the images to be classified, and achieve the effect of reducing complexity, improving classification results and improving performance.

Active Publication Date: 2011-04-27
探知图灵科技(西安)有限公司
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Problems solved by technology

How to choose a method that can effectively extract image features is very important for subsequent classification and recognition. General feature extraction methods include energy features, variance, mean, Hu moment, etc. However, a single selection of a feature cannot effectively represent the Classified images cannot reflect the effectiveness of classification features

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  • Method for sorting radar two-dimension image based on multi-dimension geometric analysis
  • Method for sorting radar two-dimension image based on multi-dimension geometric analysis
  • Method for sorting radar two-dimension image based on multi-dimension geometric analysis

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Embodiment Construction

[0024] refer to figure 1 , the steps of the present invention are as follows:

[0025] Step 1, input a sample image set, and normalize each image in the sample image set.

[0026] Assuming that the number of sample image sets to be classified is N, denoted as (x 1 , x 2 ,...,x N ); since it is a supervised learning classification algorithm, that is, the support vector machine algorithm, it is necessary to randomly select M images from N images as training sample images, denoted as (x 1 , x 2 ,...,x M );ImageSet(x 1 , x 2 ,...,x N ) pixels are normalized from 0 to 255 to 0 to 1, denoted as (z 1 ,z 2 ,...,z N ). According to Donoho's proof, the normalized image can extract more effective features.

[0027] In step 2, perform three-layer wavelet decomposition and Contourlet decomposition on each sample image after normalization, and obtain 10 wavelet decomposition subbands and 13 Contourlet decompositions respectively corresponding to a sample image.

[0028] For th...

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Abstract

The invention discloses a radar two-dimensional image classifying method based on multi-dimension geometric analysis, belongs to the technical field of image processing, and mainly overcomes the defect that an existing method can not effectively express a radar two-dimensional image. The method comprises the following steps: firstly, a sample image set is input, and each image in the sample imageset is normalized; secondly; three-layer wavelet decomposition and Contourlet decomposition are carried out on each normalized sample image to obtain ten wavelet decomposition subbands and thirteen Contourlet decomposition subbands which are respectively corresponding to the sample image; thirdly, energy characteristics are carried out on the obtained decomposition subbands and are merged by utilizing a characteristic merging method; and fourthly, the merged characteristics are classified by selecting support vector machine (SVM) algorithm. The invention has better classification accuracy rate and lower complexity, and can be used for classification of radar two-dimensional images and texture images as well as identification of bridge targets in SAR images.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to an image classification method, which can be used for classification of radar two-dimensional images and texture images and bridge target recognition in SAR images. Background technique [0002] A general two-dimensional radar image contains a lot of linear information, which may appear in any direction, any position and on different scales in the image. Wavelet can only provide a limited number of directions, so the direction information in the two-dimensional radar image cannot be fully exploited. The multi-scale geometric tool is a good helper to solve the problem, and has great potential when dealing with linear features. Wavelet has the optimal representation characteristic when approaching the objective function with one-dimensional singularity, that is, point singularity. However, in the case of high-dimensional data, wavelet cannot optimally represent some funct...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G01S13/90
Inventor 焦李成侯彪刘帆王爽刘芳杨淑媛马文萍钟桦缑水平
Owner 探知图灵科技(西安)有限公司
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