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

fYear

2013

fDate

21-26 July 2013

Firstpage

1107

Lastpage

1110

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium (IGARSS), 2013 IEEE International

Conference_Location

Melbourne, VIC

ISSN

2153-6996

Print_ISBN

978-1-4799-1114-1

Type

conf

DOI

10.1109/IGARSS.2013.6721358

Filename

6721358