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High-resolution sar image classification method based on co-sparse model

A classification method and sparse model technology, applied in character and pattern recognition, instruments, calculations, etc., can solve problems such as insufficient utilization of learned analytic operators, limited accuracy of image classification, etc., achieve fast speed, improve classification efficiency, high efficiency effect

Active Publication Date: 2019-10-25
XIDIAN UNIV
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Problems solved by technology

[0006] Although the co-sparse analytical model has been applied to the field of image classification, and the method based on the co-sparse model has shown advantages in speed, but this type of method has not been applied in the field of high-resolution SAR image classification, and the image classification method solves When co-sparse coefficients are used, the soft threshold method is simply used, and the analytical operator of learning is not fully utilized, and the accuracy of image classification is limited.

Method used

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  • High-resolution sar image classification method based on co-sparse model
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  • High-resolution sar image classification method based on co-sparse model

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Embodiment 1

[0026] The present invention is a kind of high-resolution SAR image classification method based on co-sparse model, see figure 1 , including the following steps:

[0027] Step 1. Input the image to obtain the initial pixel value matrix: select a high-resolution SAR image to be classified, such as figure 2 For the image shown in (a), read the image with Matlab software. If the input image is a three-channel RGB image, it needs to be converted into a grayscale image. If it is a single-channel grayscale image, the image’s Gray value matrix, each element in the matrix corresponds to a pixel in the image.

[0028] Take all pixels as the center to intercept M 1 × M 1 For the convenience of subsequent data processing, each pixel block is expanded into a column. Traverse all pixels in the image to get the experimental sample X∈R M×LL , M is the number of pixels in a pixel block, LL is the number of pixels in the high-resolution SAR image to be classified, that is, the initial pi...

Embodiment 2

[0042] The method for classifying high-resolution SAR images based on the co-sparse model is the same as that in Embodiment 1, wherein in step 3, the method for combining the projected subgradient and the unified row norm compact frame is used to construct the analytical operator and includes the following steps:

[0043] 3.1. Construct the initial analytical operator Ω 0

[0044] Randomly generate an overcomplete matrix D=[d of size M×N 1 , d 2 ,...,d q ,...,d N ], where d q is a column vector, 1≤q≤N, N is the dimension of the analytical operator, the size of N is manually selected according to the needs but must be greater than M, and the initial analytical operator Ω is obtained 0 ,Ω 0 is the transpose matrix of the overcomplete matrix D, that is, Ω 0 =D T . 3.2. Analytical operator projection formula by subgradient Calculate the subgradient analytical operator Ω g , 0≤i≤K max1 , K max1 In order to find the maximum number of iterations of the analytical operat...

Embodiment 3

[0055] The method for classifying high-resolution SAR images based on the co-sparse model is the same as in Embodiment 1-2, wherein in step 4, the augmented Lagrangian method is used to solve the co-sparse coefficient Z problem: s.t.Z=ΩX, including the following steps:

[0056] 4.1. Given the initial pixel value matrix X 0 =X, analytical operator Ω, initial co-sparse coefficient Z 0 =ΩX 0 , the initial parameter matrix B 0 for size with Z 0 Equal zero matrix, constant parameter λ<1, constant coefficient γ<1.

[0057] 4.2. Calculate the pixel value matrix X according to the following formula i+1 , co-sparse coefficient Z i+1 and parameter matrix B i+1 , 0≤i≤K max2 , K max2 is the maximum number of iterations to find the co-sparse coefficients.

[0058]

[0059]

[0060] in

[0061] B i+1 =B i -(Z i+1 -ΩX i+1 )

[0062] 4.3. Repeat step 4.2 until ||Z i+1 -ΩX i+1 || 2 ≥ε or i>K max2 When the iterative process ends, ε is a constant error coefficient mu...

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Abstract

The invention discloses a high-resolution SAR image classification method based on a co-sparse model. The invention solves the technical problem that the SAR image classification is limited to expressing images with a comprehensive sparse model, resulting in high classification time complexity. The process is as follows: select the initial pixel value matrix X in the SAR image to be classified; select the analytic operator to learn the initial sample; combine the projection subgradient method and the unified row norm tight frame method to learn the analytic operator Ω; use the augmented Lager The Langer method is used to solve the co-sparse coefficient Z; the co-sparse coefficient vector of each pixel corresponding to the pixel block is combined with the pixel value vector of the pixel block to obtain the feature vector; based on the SVM classifier classification, the feature vector of each pixel in the whole image is obtained The predicted label; display the predicted label result with a grayscale image. The invention can quickly obtain the sparse representation of the image, ensures the timeliness and classification accuracy of the SAR image classification, and is used for the classification of the high-resolution SAR image.

Description

technical field [0001] The invention belongs to the technical field of image processing, specifically for the classification of high-resolution SAR images, specifically a high-resolution SAR image classification method based on a co-sparse model, which is applied to the field of SAR image classification. Background technique [0002] With the gradual improvement of Synthetic Aperture Radar (SAR) imaging technology, more and more applications of SAR images in various fields have prompted further research and development of SAR image classification technology. Due to the coherent speckle noise in the original SAR image, the traditional image classification methods in the past are not applicable to SAR image classification. SAR image classification can be applied to resource detection, military reconnaissance, medical fields, crop growth monitoring, disaster hazard assessment, etc. The value and importance of SAR image applications, and SAR image classification methods need to ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62
CPCG06F18/2411
Inventor 侯彪焦李成于竞竞王爽马晶晶马文萍冯婕张小华
Owner XIDIAN UNIV
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