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- W2361457191 abstract "Spectral clustering methods have recently shown great promise for the problem of image segmentation.However,the computational demands of these approaches make them infeasible to large problems such as multispectral remote sensing images.According to the particularities of multispectral remote sensing images,a segmentation method is proposed based on spectral clustering,statistics characteristics of Contourlet coefficients and multiscale Markov model.First,the spectral clustering is implemented to the coarsest lowpass image of multispectral remote sensing image in Contourlet domain to obtain the initial segmentation result;Second,a multiscale Markov model,which contains the initial segmentation result as the coarsest scale,is constructed using mutual information to capture the relationship between Contourlet coefficients in the same scale and across scales;Third,the final segmentation result is obtained by confusing multiscale and multi-directional image information based on the multiscale Markov model.Compared with the classical spectral clustering method and the multiscale segmentation method based on HMT model in Contourlet domain,the segmentation results for both synthetic images and real multispectral remote sensing images show that the proposed method not only has better performance in edges preservation and noise sensitivity,but also has lower misclassification probability and running time,and it can achieve satisfactory segmentation results." @default.
- W2361457191 created "2016-06-24" @default.
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- W2361457191 date "2013-01-01" @default.
- W2361457191 modified "2023-09-28" @default.
- W2361457191 title "Segmentation of multispectral remote sensing image based on spectral clustering and multiscale Markov model in Contourlet domain" @default.
- W2361457191 hasPublicationYear "2013" @default.
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