期刊
1ST ISPRS INTERNATIONAL CONFERENCE ON GEOSPATIAL INFORMATION RESEARCH
卷 40, 期 2/W3, 页码 65-68出版社
COPERNICUS GESELLSCHAFT MBH
DOI: 10.5194/isprsarchives-XL-2-W3-65-2014
关键词
SAR; Centroidal Voronoi Tessellation; Segmentation; Clustering; Gamma Distribution
In this research, a fast, adaptive and user friendly segmentation methodology is developed for highly speckled SAR images. The developed region based centroidal Voronoi tessellation (R-BCVT) algorithm is a kind of polygon-based clustering approach in which the algorithm attempts to (1) split the image domain into j numbers of centroidal Voronoi polygons (2) assign each polygon a label randomly, then (3) classify the image into k cluster iteratively to satisfy optimum segmentation, and finally a k-mean clustering method refine the detected boundaries of homogeneous regions. The advantages of the novel method arise from adaptively, simplicity and rapidity as well as low sensitivity of the model to speckle noise.
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