Spatial regularization of pixel-based classification maps by a two-step MRF method

Markov random field (MRF)based spatial regularizing methodology can improve the maps by imposing a spatial smoothness prior on the image grid, but also leads to oversmoothing at image boundary areas. This problem is caused by the reason that classic isotropous smoothness prior cannot take local discontinuities into account. In this context, this paper proposes a novel two-step MRF regularization algorithm, which addresses the problem by combining both spectral and class cost in spatial modules. The developed MRF method first establishes a spatial energy function integrating local spectral dissimilarity to smooth the initial classification map while preserving object boundaries. Second, a new anisotropic spatial energy function integrating the class co-occurrence dependency is constructed to regularize pixels around object boundaries. The effectiveness of the proposed MRF method is validated by a series of remote sensing data sets. The obtained results indicate that the method can significantly improve the classification accuracy with regards to traditional MRF classification models.