Gibbs random field models: a toolbox for spatial information extraction
In this paper, we present Gibbs random field models in the form of a powerful toolbox for spatial information extraction from remote sensing images. These models are defined via parametrised energy functions that characterise local interactions between neighbouring pixels. After shortly revisiting the information theoretical concept and defining a family of Gibbs models, we give a tour through examples of different kinds of spatial information extraction. These examples range from parameter estimation and analysis, via selection of the model that best describes the image data, up to the segmentation of the whole image into regions with uniform properties of the model. Finally, the concept of across-image segmentation of spatial information leads to an application for content-based queries from remote sensing image archives.
remote sensing image processing image segmentation