Volume XL-2/W3
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W3, 65-68, 2014
https://doi.org/10.5194/isprsarchives-XL-2-W3-65-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-2/W3, 65-68, 2014
https://doi.org/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

22 Oct 2014

AN ADAPTIVE POLYGONAL CENTROIDAL VORONOI TESSELLATION ALGORITHM FOR SEGMENTATION OF NOISY SAR IMAGES

G. Askari1, Y. Li2, and R. MoezziNasab1 G. Askari et al.
  • 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.