An Adaptive MeanShift Segmentation Method of Remote Sensing Images Based on Multi-Dimension Features

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.