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Quantum multi-target clustering-based remote sensing image segmentation method

A remote sensing image and multi-target technology, which is applied in image enhancement, image data processing, instruments, etc., can solve the problems of single evaluation index, high computational complexity, and poor detail retention performance, so as to improve segmentation accuracy and fast segmentation speed , The effect of diversification of evaluation indicators

Inactive Publication Date: 2012-07-11
XIDIAN UNIV
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Problems solved by technology

The invention realizes the remote sensing image segmentation through the clustering method combining quantum computing and multi-objective optimization, so as to solve the shortcomings of the existing image segmentation technology such as single evaluation index, high computational complexity, and poor detail retention performance, and improves the efficiency of image segmentation. precision

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  • Quantum multi-target clustering-based remote sensing image segmentation method
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  • Quantum multi-target clustering-based remote sensing image segmentation method

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Embodiment Construction

[0045] Step 1, input the remote sensing image to be segmented

[0046] Step 2, extract the image features to be segmented

[0047] First, the wavelet eigenvector is obtained by using the wavelet decomposition method;

[0048] The wavelet decomposition method uses a three-level wavelet transform with a window size of 16×16 on the image to obtain a 10-dimensional wavelet feature vector composed of subband coefficients.

[0049] Then, use the gray level co-occurrence matrix method to extract the texture feature vector;

[0050] The steps of the gray level co-occurrence matrix method are as follows:

[0051] First quantize the image to be processed into 16 gray levels, and then set the angles between the two pixel points and the horizontal axis to be 0°, 45°, 90° and 135° in turn, and calculate the four directions according to the following formula The gray level co-occurrence matrix of :

[0052] P(i,j)=#{(x 1 ,y 1 ), (x 2 ,y 2 )∈M×N|f(x 1 ,y 1 ) = r, f(x 2 ,y 2 )=s} ...

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Abstract

The invention discloses a quantum multi-target clustering-based remote sensing image segmentation method, which mainly solves the problems of single evaluation index, high calculation complexity and poor segmentation effect in the conventional image segmentation technology. The quantum multi-target clustering-based remote sensing image segmentation method comprises the following implementation steps of: (1) inputting a remote sensing image to be segmented; (2) extracting the characteristics of the image to be segmented; (3) generating clustering data; (4) randomly generating an initial quantum population to finish initialization; (5) acquiring a binary population; (6) calculating an individual fitness value; (7) selecting non-dominated sorting; (8) evolving the population; (9) judging whether to meet stop condition; (10) distributing a category label; (11) generating the best individual; and (12) outputting a segmentation image. According to the quantum multi-target clustering-based remote sensing image segmentation method, the characteristics of each pixel of the image are extracted; remote sensing image segmentation is realized through a quantum calculation and multi-target optimization combined clustering method; and the quantum multi-target clustering-based remote sensing image segmentation method has the advantages of high segmentation accuracy and accurate edge positioning and can be used for segmenting a complex image.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a quantum multi-objective-based clustering and segmentation method in the technical field of remote sensing image segmentation. The invention can be used to segment optical remote sensing images and synthetic aperture radar SAR images to achieve the purpose of target recognition. Background technique [0002] Image segmentation is the technology and process of dividing an image into several specific regions with unique properties and proposing objects of interest. At present, people mostly use methods based on cluster analysis for image segmentation. Segmenting an image with a method based on cluster analysis is to represent the pixels in the image space with corresponding feature space points, segment the feature space according to their aggregation in the feature space, and then map them back to the original image space to achieve image segmentation. the goal o...

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Application Information

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IPC IPC(8): G06T5/00G06N3/00
Inventor 李阳阳焦李成冯士霞刘芳公茂果尚荣华马文萍于昕
Owner XIDIAN UNIV
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