結合BIC準則和ECM算法的可變類SAR影像分割
發(fā)布時間：2018-12-22 08:23
【摘要】：為實現(xiàn)合成孔徑雷達(SAR)影像分割中類別數(shù)的自動確定,提出一種基于貝葉斯信息準則(BIC)的可變類SAR影像分割算法.該算法以Gamma分布建模SAR影像同質區(qū)域內部像素光譜測度的統(tǒng)計分布特性;結合BIC準則構建整幅SAR影像似然函數(shù)模型;并在此模型中引入類別數(shù)補償項,繼而提高BIC測度對影像分割結果的描述精度.采用期望條件最大化(ECM)算法估計其模型參數(shù);通過遍歷所有可能類別數(shù),取最小BIC值對應的類別數(shù)作為最佳類別數(shù).采用提出的算法分割模擬和真實SAR影像,模擬SAR影像分割結果的定性和定量分析表明,基于BIC準則的ECM算法可以實現(xiàn)類別數(shù)的自動確定,并可得到最優(yōu)分割結果.通過對真實SAR影像分割結果的定性評價,進而證明了可變類SAR影像分割算法的準確性和可行性.
[Abstract]:In order to automatically determine the number of categories in (SAR) image segmentation of synthetic Aperture Radar (SAR), a variable class SAR image segmentation algorithm based on Bayesian Information Criterion (BIC) is proposed. The algorithm uses Gamma distribution to model the statistical distribution of the pixel spectral measure in the homogeneous region of SAR image, and combines the BIC criterion to construct the likelihood function model of the whole SAR image. In this model, the category number compensation term is introduced to improve the accuracy of the BIC measure in describing the image segmentation results. The expected condition maximization (ECM) algorithm is used to estimate the model parameters, and the number of classes corresponding to the minimum BIC value is taken as the best category number by traversing all possible categories. The qualitative and quantitative analysis of the segmentation results of simulated SAR images using the proposed algorithm shows that the ECM algorithm based on the BIC criterion can automatically determine the number of categories and obtain the optimal segmentation results. Through the qualitative evaluation of the real SAR image segmentation results, the accuracy and feasibility of the variable class SAR image segmentation algorithm are proved.
【作者單位】： 遼寧工程技術大學測繪與地理科學學院遙感科學與應用研究所;
【基金】：國家自然科學基金青年基金項目(41301479) 遼寧省自然科學基金項目(2015020090)
【分類號】：TN957.52
本文編號：2389563
[Abstract]:In order to automatically determine the number of categories in (SAR) image segmentation of synthetic Aperture Radar (SAR), a variable class SAR image segmentation algorithm based on Bayesian Information Criterion (BIC) is proposed. The algorithm uses Gamma distribution to model the statistical distribution of the pixel spectral measure in the homogeneous region of SAR image, and combines the BIC criterion to construct the likelihood function model of the whole SAR image. In this model, the category number compensation term is introduced to improve the accuracy of the BIC measure in describing the image segmentation results. The expected condition maximization (ECM) algorithm is used to estimate the model parameters, and the number of classes corresponding to the minimum BIC value is taken as the best category number by traversing all possible categories. The qualitative and quantitative analysis of the segmentation results of simulated SAR images using the proposed algorithm shows that the ECM algorithm based on the BIC criterion can automatically determine the number of categories and obtain the optimal segmentation results. Through the qualitative evaluation of the real SAR image segmentation results, the accuracy and feasibility of the variable class SAR image segmentation algorithm are proved.
【作者單位】： 遼寧工程技術大學測繪與地理科學學院遙感科學與應用研究所;
【基金】：國家自然科學基金青年基金項目(41301479) 遼寧省自然科學基金項目(2015020090)
【分類號】：TN957.52
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