In order to adapt different scale land cover segmentation, an optimized approach under the guidance of k-means clustering for multi-scale segmentation is proposed. At first, small scale segmentation and k-means clustering are used to process the original images; then the result of k-means clustering is used to guide objects merging procedure, in which Otsu threshold method is used to automatically select the impact factor of k-means clustering; finally we obtain the segmentation results which are applicable to different scale objects. FNEA method is taken for an example and segmentation experiments are done using a simulated image and a real remote sensing image from GeoEye-1 satellite, qualitative and quantitative evaluation demonstrates that the proposed method can obtain high quality segmentation results.

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approach otsu threshold method impact factor of kmeans kmeans clustering real remote sensing image simulated image multiscale segmentation objects merging geoeye1

WANG Huixian,JIN Huijia,WANG Jiaolong,JIANG Wanshou,.Optimization Approach for Multi-scale Segmentation of Remotely Sensed Imagery under k-means Clustering Guidance. 44 (5),.

WANG Huixian,JIN Huijia,WANG Jiaolong,JIANG Wanshou,"Optimization Approach for Multi-scale Segmentation of Remotely Sensed Imagery under k-means Clustering Guidance" 44

WANG Huixian,JIN Huijia,WANG Jiaolong,JIANG Wanshou,"Optimization Approach for Multi-scale Segmentation of Remotely Sensed Imagery under k-means Clustering Guidance"