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