Title :
The application of rough set theory in remote sensing image classification
Author :
Nan, Zheng ; Haiyu, Li ; Yu, Zhou Mai
Abstract :
Rough set theory is a relatively new mathematical tool to deal with imprecise, incomplete and inconsistent data. The content of this paper is twofold. First, to decrease computational time, band reduct is performed on multispectral remote sensing image using rough set and information entropy. Second, image classification is obtained after band reduction, Gaussian mixture model and EM algorithm are considered. The algorithm designed in this paper can make bands reduction and multispectral remote sensing image classification unsupervised. Experimental results show that the proposed method did have effective and valid performance.
Keywords :
Gaussian processes; entropy; expectation-maximisation algorithm; image classification; remote sensing; rough set theory; EM algorithm; Gaussian mixture model; band reduction; information entropy; multispectral remote sensing image; remote sensing image classification; rough set theory; Algorithm design and analysis; Digital images; Ecosystems; Image classification; Information entropy; Information systems; Military computing; Remote sensing; Set theory; Uncertainty; Classification; Remote Sensing Image; Rough Set Theory;
Conference_Titel :
E-Health Networking, Digital Ecosystems and Technologies (EDT), 2010 International Conference on
Conference_Location :
Shenzhen
Print_ISBN :
978-1-4244-5514-0
DOI :
10.1109/EDT.2010.5496550