Gaussian Mixture Model Segmentation Algorithm for Remote Sensing Image

Abstract

The paper proposed a novel method for remote sensing image segmentation based on mixture model. The remote sensing image data would be considered as Gaussian mixture model. The image segmentation result was corresponding to the image label field which was a Markov Random Field(MRF). So, the image segmentation procedure was transformed to a Maximum A Posteriori(MAP) problem by Beyesian theorem. The intensity difference and the spatial distance between the two pixels in the same clique were employed in the potential function. The Iterative Conditional Model(ICM) is employed to solve MAP. In the experiments, the method is compared with the traditional MRF segmentation method using ICM and simulate annealing(SA). The experiments proved that this algorithm was more efficient than the traditional MRF one.