利用高斯混合模型的多光譜圖像模糊聚類分割
發(fā)布時間：2019-08-19 10:22
【摘要】：針對傳統(tǒng)分割算法難以實現(xiàn)高分辨率多光譜圖像分割的問題,本文提出一種利用高斯混合模型的多光譜圖像模糊聚類分割算法。該算法采用高斯混合模型定義像素對類屬的非相似性測度,由于該算法具有高精度擬合數(shù)據(jù)統(tǒng)計分布能力,故可以有效剔除噪聲對分割結(jié)果的影響。同時,引入隱馬爾科夫隨機場(Hidden Markov Random Field,HMRF)定義鄰域作用的先驗概率,并將其作為各高斯分量權值以及KL(Kullback-Leibler)信息中控制聚類尺度的參數(shù),從而增強了算法對復雜場景遙感圖像的魯棒性,進一步提高了算法的分割精度。對模擬圖像和高分辨多光譜圖像分割結(jié)果進行了定性定量分析。實驗結(jié)果表明:模擬圖像的總精度達96.8%以上。這驗證了本文算法在分割高分辨率多光譜圖像時具有保留細節(jié)信息的能力,而且也證實了算法的有效性和可行性。該算法能夠?qū)崿F(xiàn)高分辨率多光譜圖像的精確分割。
[Abstract]:In order to solve the problem that traditional segmentation algorithm is difficult to achieve high resolution multispectral image segmentation, this paper proposes a multi-spectral image fuzzy clustering segmentation algorithm based on Gaussian mixture model. The algorithm uses Gaussian mixture model to define the non-similarity measure of pixel to class. Because the algorithm has the ability of high precision fitting data statistical distribution, it can effectively eliminate the influence of noise on the segmentation results. At the same time, the hidden Markov random field (Hidden Markov Random Field,HMRF) is introduced to define the prior probability of neighborhood action, and it is used as the weight of each Gaussian component and the parameter to control the clustering scale in KL (Kullback-Leibler) information, which enhances the robustness of the algorithm to remote sensing images of complex scenes and further improves the segmentation accuracy of the algorithm. The segmentation results of simulated images and high resolution multispectral images are qualitatively and quantitatively analyzed. The experimental results show that the total accuracy of the simulated image is more than 96.8%. This verifies the ability of the proposed algorithm to preserve detail information in the segmentation of high-resolution multispectral images, and also verifies the effectiveness and feasibility of the algorithm. The algorithm can realize the accurate segmentation of high resolution multispectral images.
【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(41301479、41271435) 遼寧省自然科學基金(2015020090)
【分類號】：TP391.41
[Abstract]:In order to solve the problem that traditional segmentation algorithm is difficult to achieve high resolution multispectral image segmentation, this paper proposes a multi-spectral image fuzzy clustering segmentation algorithm based on Gaussian mixture model. The algorithm uses Gaussian mixture model to define the non-similarity measure of pixel to class. Because the algorithm has the ability of high precision fitting data statistical distribution, it can effectively eliminate the influence of noise on the segmentation results. At the same time, the hidden Markov random field (Hidden Markov Random Field,HMRF) is introduced to define the prior probability of neighborhood action, and it is used as the weight of each Gaussian component and the parameter to control the clustering scale in KL (Kullback-Leibler) information, which enhances the robustness of the algorithm to remote sensing images of complex scenes and further improves the segmentation accuracy of the algorithm. The segmentation results of simulated images and high resolution multispectral images are qualitatively and quantitatively analyzed. The experimental results show that the total accuracy of the simulated image is more than 96.8%. This verifies the ability of the proposed algorithm to preserve detail information in the segmentation of high-resolution multispectral images, and also verifies the effectiveness and feasibility of the algorithm. The algorithm can realize the accurate segmentation of high resolution multispectral images.
【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(41301479、41271435) 遼寧省自然科學基金(2015020090)
【分類號】：TP391.41
【參考文獻】
相關期刊論文 前10條
1 趙雪梅;李玉;趙泉華;;結(jié)合馬爾可夫高斯模型的雙鄰域模糊聚類分割算法[J];計算機輔助設計與圖形學學報;2016年04期
2 趙雪梅;李玉;趙泉華;;基于隱馬爾可夫高斯隨機場模型的模糊聚類高分辨率遙感影像分割算法[J];電子學報;2016年03期
3 蔣璐媛;肖鵬峰;馮學智;李云;朱榴駿;;基于亞分數(shù)混淆矩陣的中國典型區(qū)大尺度土地覆蓋數(shù)據(jù)集評價[J];遙感技術與應用;2015年02期
4 王醒策;文蕾;武仲科;周明全;田l，
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