Wishart and SVM (support vector machine)-based polarimetric SAR (synthetic aperture radar) image classification method

A classification method and image technology, applied in character and pattern recognition, instruments, computer parts, etc., to achieve the effect of high accuracy, good universality and generalization, and good classification effect

Active Publication Date: 2015-03-11
XIDIAN UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

[0005] The purpose of the present invention is to overcome the shortcomings of the above-mentioned prior art that polarimetric SAR data cannot achieve higher classification accura

Method used

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  • Wishart and SVM (support vector machine)-based polarimetric SAR (synthetic aperture radar) image classification method
  • Wishart and SVM (support vector machine)-based polarimetric SAR (synthetic aperture radar) image classification method
  • Wishart and SVM (support vector machine)-based polarimetric SAR (synthetic aperture radar) image classification method

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Embodiment 1

[0028] The invention is a polarization synthetic aperture radar SAR image classification method based on Wishart and SVM. Refer to attached figure 1 , the specific implementation steps of the present invention are described in further detail:

[0029] Step 1, input image, input an optional polarization synthetic aperture radar SAR image to be classified, specifically figure 2 The L-band multi-look polarimetric SAR image of Flevoland, Netherlands area obtained in 1989 is shown.

[0030] Step 2. Filtering. In the specific simulation experiment, the polarized refined Lee filtering method with the filter window size of 3*3, 5*5, 7*7 and 9*9 is used respectively to filter the polarization synthetic aperture radar SAR image to be classified. filtering to remove coherent speckle noise, to obtain a filtered polarimetric SAR image, and to obtain a coherence matrix of the filtered polarimetric SAR image. In this embodiment, a filter window with a size of 7*7 is selected to remove co...

Embodiment 2

[0048] The polarization synthetic aperture radar SAR image classification method based on Wishart and SVM is the same as embodiment 1, wherein the Cloude decomposition described in step 3 includes the following steps:

[0049] 3.1. Extract the coherence matrix of the filtered polarimetric SAR image;

[0050] 3.2. Decompose the eigenvalue of the coherence matrix to obtain the eigenvalue λ of the coherence matrix 1 ,λ 2 ,λ 3 ;

[0051] 3.3. Using the obtained eigenvalue λ 1 ,λ 2 ,λ 3 , calculate the scattering entropy H, scattering angle alpha and total power span of each pixel according to the following formula,

[0052] H = - Σ i = 1 3 p i log ( p i ) , 0 ≤ H ≤ 1 ...

Embodiment 3

[0058] The polarization synthetic aperture radar SAR image classification method based on Wishart and SVM is the same as embodiment 1-2, wherein the similarity matrix W of the computing feature set F described in step 4 F Include the following steps:

[0059] 4.1. Construct feature set F=[H alpha span];

[0060] 4.2. Calculate the similarity matrix W of the feature set F F ;

[0061] W F = exp ( - d F ( F i , F j ) 2 σ 1 2 )

[0062] where d F (F i , F j )=||F i...

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Abstract

The invention discloses a Wishart and SVM (support vector machine)-based polarimetric SAR (synthetic aperture radar) image classification method. The technical problems of low classification accuracy and low classification efficiency of a conventional polarimetric SAR classification method in case of fewer training samples are mainly solved. The method is implemented by the following steps: inputting an image; performing filtration; performing Cloude decomposition; calculating a similar matrix of a characteristic set F; calculating a similar matrix of a coherence matrix characteristic set T; calculating a final similar matrix; performing classification by virtue of an SVM; calculating accuracy. When the method is used for classification, the problem of more misclassified points caused by noise in the prior art is solved, crosstalk between polarization channels can be avoided, polarimetric information and counting correlation can be maintained, the contour and edge of a polarimetric SAR image are clearer, the quality of the classified image is improved, higher polarimetric SAR image classification accuracy and higher noise adaptability are achieved, and the method can be used for the target identification and tracking of the polarimetric SAR image.

Description

technical field [0001] The present invention belongs to the technical field of image processing, and further relates to the technical field of machine learning and image classification, specifically a polarimetric SAR image classification method based on Wishart and SVM, which can be applied to the object classification of polarimetric SAR images to realize target recognition with tracking. Background technique [0002] Polarization SAR is a high-resolution active microwave remote sensing imaging radar, which has the advantages of all-day, all-weather, and high resolution. It has been extensively studied in agriculture, forestry, ocean, military and other fields. There are many methods for polarimetric SAR image classification. According to the different classifiers used, they can be divided into statistics, neural networks, support vectors, decision trees, etc.; according to whether spatial information is used, they can be divided into pixel-based and region-based; accordin...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06K9/46
CPCG06F18/2411G06F18/2415
Inventor 焦李成李玲玲党晓婉屈嵘杨淑媛候彪王爽刘红英熊涛马文萍马晶晶
Owner XIDIAN UNIV
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