Title :
The spectral-spatial classification of hyperspectral images based on Hidden Markov Random Field and its Expectation-Maximization
Author :
Ghamisi, Pedram ; Benediktsson, Jon Atli ; Ulfarsson, Magnus Orn
Author_Institution :
Univ. of Iceland, Reykjavik, Iceland
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.
Keywords :
edge detection; expectation-maximisation algorithm; hidden Markov models; hyperspectral imaging; image classification; support vector machines; HMRF-EM; SVM classifier; Sobel edge detector; edge preservation; expectation-maximization; hidden Markov random field; hyperspectral images; spatial information; spectral information; spectral-spatial classification; support vector machine; Accuracy; Hidden Markov models; Hyperspectral imaging; Image edge detection; Image segmentation; Support vector machines; Hidden Markov Random Field; hyperspectral image analysis; image segmentation;
Conference_Titel :
Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International
Conference_Location :
Melbourne, VIC
Print_ISBN :
978-1-4799-1114-1
DOI :
10.1109/IGARSS.2013.6721358
