Adaptive Gaussian Mixture Estimation and Its Application to Unsupervised Classification of Remotely Sensed Images

This paper addresses unsupervised statistical classification to remotely sensed images based on mixture estimation. The application of the well-known technique, Expectation Maximization (EM) algorithm to multi-dimensional image data is to be investigated, where Gaussian mixture is assumed. The number of classes can be estimated via Neyman-Pearson detection theory-based eigen-thresholding approach, which is used as a reference value in the learning process. Since most remotely sensed images are nonstationary, adaptive EM (AEM) algorithm will also be explored by localizing the estimation process. Remote sensing data is used in the experiments for performance analysis. In particular, comparative study will be conducted to quantify the improvement from the adaptive EM algorithm.