SAR image segmentation by selected principal components and kernel graph cuts ensembles

Graph cuts (GC) have become one of the most important schemes in image segmentation. Recently, some researches tend to study GC and kernel mapping of the image data. However, the existing kernel GC (KGC) image segmentation not only suffers from different types of noise, but also suffers from the settings of the regularising parameter, which is used to balance the edge and region terms in the existing KGC. This letter is to investigate the image segmentation via principal component analysis and kernel graph cuts ensemble. The principal components are selected by the strength of introduced noise, which is estimated by the maximum statistics of variation coefficient. Besides, the regularization term of KGC is difficult to choose the appropriate scaling factor value for the regularization term, and it is always obtained by experience. Here, we present KGC ensemble strategy to combine the segmenting results under different settings of scaling parameter. In addition, the KGC is implemented in the projected lower dimensional subspace by selected principal components to remove the noise element. Experiments on SAR images indicate the advantages of the proposed algorithm over the existing KGC, two variants of fuzzy c-means not only in class completeness but also in the boundary discrimination.