Sub-pixel Land Cover Mapping Based on Markov Random Field Models

Occurrence of mixed pixels in remote sensing images is a common phenomenon particularly in coarse spatial resolution images. In these cases, sub-pixel or soft classification may be preferred over conventional hard classification. However, sub-pixel classification fails to account for the spatial distribution of class proportions within the pixel. A better approach may be to generate a land cover map at a finer resolution from the coarse resolution images based on image models that accurately characterize the spatial distribution of the classes. The resulting fine resolution map may be called a sub-pixel or super resolution map. In this paper, an approach based on Markov random fields is introduced to generate sub-pixel land cover maps from remote sensing images dominated by mixed pixels.