Model based simulation of multispectral images based on remotely sensed data
The assumption that a real scene is a single sample under an assumed model allows simulated scenes with stochastic properties similar to those of the actual scene, which can be utilized for evaluation and validation of proposed models and investigation of the reliability of the results. With this purpose, an appropriate model-based approach to account for stochastic properties of the scenes is required. This research focused on development of a hierarchical stochastic model to characterize processes observed in remotely sensed imagery and simulation of scenes based on the developed models to provide a general methodology for dynamic spatial landscape modeling and a variety of image processing research. The new model is based on a comprehensive stochastic representation of the scene. At the higher level, region formation process is modeled as a large scale characteristic of the scene employing a Markov random field. The boundary variation around the adjacent regions is dealt with using fuzzy approach. The natural variability within each region is represented at the lower level of the hierarchy. For this, two approaches based on the different assumptions are suggested in modeling the statistical features of continuous radiance field. In the first model, pixel intensities are assumed to be independently and identically distributed and the second model employs a continuous random field including possible contextual information. Finally, this integrated simulation process forms the multispectral images.
markov random field scene simulation multivariate gaussian mixture model gaussian markov random field multiresolution framework