The spectral-spatial classification of hyperspectral images based on Hidden Markov Random Field and its Expectation-Maximization

Abstract

In this work, a new framework for accurate classification of hyperspectral images is proposed. The new method is based on Hidden Markov Random Field and its Expectation Maximization (HMRF-EM) and Support Vector Machine (SVM) classifier. In order to preserve edges in final map, the Sobel edge detector is used. Result confirms that the combination of the spectral and spatial information can significantly improve results compared to the standard SVM method.