an improved algorithm based on minimum spanning tree for multi-scale segmentation of remote sensing imagery

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ID: 144998
2015
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Abstract
As the basis of object-oriented information extraction from remote sensing imagery,image segmentation using multiple image features,exploiting spatial context information, and by a multi-scale approach are currently the research focuses. Using an optimization approach of the graph theory, an improved multi-scale image segmentation method is proposed. In this method, the image is applied with a coherent enhancement anisotropic diffusion filter followed by a minimum spanning tree segmentation approach, and the resulting segments are merged with reference to a minimum heterogeneity criterion.The heterogeneity criterion is defined as a function of the spectral characteristics and shape parameters of segments. The purpose of the merging step is to realize the multi-scale image segmentation. Tested on two images, the proposed method was visually and quantitatively compared with the segmentation method employed in the eCognition software. The results show that the proposed method is effective and outperforms the latter on areas with subtle spectral differences.
Reference Key
hui2015actaan Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;LI Hui;TANG Yunwei;LIU Qingjie;DING Haifeng;JING Linhai
Journal Phytochemistry
Year 2015
DOI
10.11947/j.AGCS.2015.20140060
URL
http://html.rhhz.net/CHXB/html/2015-7-791.htm
Keywords
cartography remotely sensed imagerymathematical geography

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