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Remotely sensed imagery segmentation based on the hierarchical representation of image content

Zhang Xueliang,Xiao Pengfeng,Feng Xuezhi Department of Geographic Information Science,Nanjing University,Nanjing 210093,China  
A segmentation method for high-spatial resolution remotely sensed images segmentation based on the hierarchical representation of the image content is proposed in this paper.The hierarchical representation is built through a hierarchical merging process based on an initial segmentation.First,we build the region adjacency graph(RAG) on which a Markov random field(MRF) is defined.Then the hierarchical merging process,where the merging criterion includes multi-spectral,edge and shape features,is applied and recorded on the RAG to obtain the hierarchical representation.It is quite efficient to produce a number of segmentation results with different precisions using the relationship of different objects in the hierarchical representation.Experimental results on QuickBird images show that the proposed method can greatly improve the segmentation efficiency and produce high quality results.
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