Fast algorithm based on superpixel-level conditional triplet Markov field for successive-approximation resistor image segmentation

Conditional random field (CRF) is a useful tool for optical and remote sensing image segmentation for its ability of incorporating the feature and texture information. However, its application is restricted in successive-approximation resistor (SAR) image segmentation, since SAR images often contain complex non-stationary contents. The triplet Markov field (TMF) model improves the non-stationary image segmentation ability by introducing an auxiliary field to characterise different stationary parts in non-stationary image. Combining the advantages of CRF and TMF, the pixel-level conditional TMF (CTMF) had been proposed. To further improve the segmentation efficiency, a superpixel-level CTMF (SL-CTMF) is proposed in this study. The superpixel representation comes from the improved TurboPixels algorithm and the superpixel representation has better performance in edge location. The auxiliary field U in SL-CTMF is reconstructed on superpixels. With the superpixel-level feature and texture information, the unary and pairwise potentials are derived. Finally, SL-CTMF is applied to real SAR image segmentation with the maximum posterior marginal inference. The experimental results demonstrate the accuracy and the efficiency of the proposed method on SAR images.