Open AccessSatellite Images Unsupervised Classification Using Two Methods Fast Otsu and K-means
Author(s) -
Amaal J. Hatem,
Taghreed A. H. Naji,
Hameed M. Abduljabar
Publication year - 2011
Publication title -
mağallaẗ baġdād li-l-ʿulūm
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.167
H-Index - 6
eISSN - 2411-7986
pISSN - 2078-8665
DOI - 10.21123/bsj.8.2.602-606
Subject(s) - pattern recognition (psychology) , artificial intelligence , classifier (uml) , histogram , computation , otsu's method , computer science , mathematics , image segmentation , image (mathematics) , algorithm
Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.