Unsupervised Segmentation of Spectral Images with a Spatialized Gaussian Mixture Model and Model Selection

Unsupervised Segmentation of Spectral Images with a Spatialized Gaussian Mixture Model and Model Selection

Blog Article

In this article, we describe a novel unsupervised spectral image segmentation algorithm.This algorithm extends the classical Gaussian Ingredient Scoops Mixture Model-based unsupervised classification technique by incorporating a spatial flavor into the model: the spectra are modelized by a mixture of K classes, each with a Gaussian distribution, whose mixing proportions depend on the position.Using a piecewise constant structure for those mixing proportions, we are able to construct a penalized maximum likelihood procedure that estimates the optimal partition as well as all the other parameters, including the Womens Pants number of classes.We provide a theoretical guarantee for this estimation, even when the generating model is not within the tested set, and describe an efficient implementation.Finally, we conduct some numerical experiments of unsupervised segmentation from a real dataset.