Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W3, 65-68, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-2-W3/65/2014/
doi:10.5194/isprsarchives-XL-2-W3-65-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
22 Oct 2014
AN ADAPTIVE POLYGONAL CENTROIDAL VORONOI TESSELLATION ALGORITHM FOR SEGMENTATION OF NOISY SAR IMAGES
G. Askari1, Y. Li2, and R. MoezziNasab1 1School of Earth Science, Damghan University, Damghan, 36716-41167, Iran
2Institute for Remote Sensing Science and Application, School of Geomatics, Liaoning Technical University,Fuxin, Liaoning 123000, China
Keywords: SAR, Centroidal Voronoi Tessellation, Segmentation, Clustering, Gamma Distribution Abstract. 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.
Conference paper (PDF, 1918 KB)


Citation: Askari, G., Li, Y., and MoezziNasab, R.: AN ADAPTIVE POLYGONAL CENTROIDAL VORONOI TESSELLATION ALGORITHM FOR SEGMENTATION OF NOISY SAR IMAGES, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W3, 65-68, doi:10.5194/isprsarchives-XL-2-W3-65-2014, 2014.

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