| 多维特征自适应MeanShift遥感图像分割方法 |
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| 引用本文： | 周家香,朱建军,梅小明,马慧云.多维特征自适应MeanShift遥感图像分割方法[J].武汉大学学报(信息科学版),2012,37(4):419-422,440. |
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| 作者姓名： | 周家香 朱建军 梅小明 马慧云 |
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| 作者单位： | 中南大学有色金属成矿预测教育部重点实验室,长沙市麓山南路932号,410083／中南大学地球科学与信息物理学院,长沙市麓山南路932号,410083 |
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| 基金项目： | 国家自然科学基金青年科学基金资助项目，国家863计划重点资助项目，武汉大学测绘遥感信息工程国家重点实验室开放研究基金资助项目 |
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| 摘 要： | 针对MeanShift算法分割遥感图像的自动化程度和精度不高的问题，提出一种多特征自适应Mean—Shift遥感图像分割方法。3组实验结果表明，本方法相比EDISON软件能得到更好的分割效果，且能在一定程度上提高遥感影像分割的自动化。
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| 关 键 词： | 遥感影像 自适应MeanShift 图像分割 带宽 纹理特征 |
An Adaptive MeanShift Segmentation Method of Remote Sensing Images Based on Multi-Dimension Features |
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| ZHOU Jiaxiang,ZHU Jianjun,MEI Xiaoming,MA Huiyun.An Adaptive MeanShift Segmentation Method of Remote Sensing Images Based on Multi-Dimension Features[J].Geomatics and Information Science of Wuhan University,2012,37(4):419-422,440. |
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| Authors: | ZHOU Jiaxiang ZHU Jianjun MEI Xiaoming MA Huiyun |
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| Institution: | 1,2(1 Key Laboratory of Metallogenic Prediction of Nonferrous Metals,Ministry of Education, Central South University,932 South Lushan Road,Changsha 410083,China)(2 School of Geosciences and Info-Physics,Central South University,932 South Lushan Road,Changsha 410083,China) |
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| Abstract: | Due to low segmentation efficiency and low accuracy of Mean-Shift algorithm,this paper puts forward to an adaptive Mean-Shift segmentation method of remote sensing images.Firstly,location features,multi-band spectrum principal components and texture features are extracted to form multi-dimension feature spaces.Then,based on classical Mean Shift clustering algorithm,initial clustering images are got by using less fixed space bandwidths and global optimal spectrum bandwidths that are estimated by plug-in rules.Mean space distance,mean spectrum distance and texture distance are calculated for each region in the initial clustering images,and used for space bandwidths,spectrum bandwidths and texture bandwidths of sequential clustering.Further,multi-dimension feature Mean-Shift Clustering was implemented by using calculated bandwidths.Lastly,the clustered regions are combined to get segmentation images.Three experiment results of remote sensing images show that the proposed method in this paper are better than EDISON software,and to some extent improve the efficiency of segmentation of remote sensing images. |
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| Keywords: | remote sensing images adaptive MeanShift image segmentation bandwidthtexture feature |
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