Decision fusion for supervised and unsupervised hyperspectral image classification

Author

Yang, He ; Ma, Ben ; Du, Qian

Author_Institution

Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA

Volume

4

fYear

2009

fDate

12-17 July 2009

Abstract

A decision fusion approach is proposed to combine the results from supervised and unsupervised classifiers. The final output takes advantage of the power of a support vector machine based supervised classification in class separation and the capability of the unsupervised K-means classifier in reducing spectral variation impact in homogeneous regions. This approach simply adopts the majority voting rule, but can achieve the same objective of object-based classification.

Keywords

geophysical image processing; image classification; image fusion; remote sensing; support vector machines; class separation; decision fusion; majority voting rule; object-based classification; spectral variation impact; supervised hyperspectral image classification; support vector machine; unsupervised K-means classifier; unsupervised hyperspectral image classification; Hyperspectral imaging; Hyperspectral sensors; Image classification; Image segmentation; Roads; Satellites; Support vector machine classification; Support vector machines; Training data; Voting; Classification; decision level fusion; hyperspectral imagery;

fLanguage

English

Publisher

ieee

Conference_Titel

Geoscience and Remote Sensing Symposium,2009 IEEE International,IGARSS 2009

Conference_Location

Cape Town

Print_ISBN

978-1-4244-3394-0

Electronic_ISBN

978-1-4244-3395-7

Type

conf

DOI

10.1109/IGARSS.2009.5417535

Filename

5417535