DocumentCode
2138657
Title
Unsupervised classification using spatial region growing segmentation and fuzzy training
Author
Lee, Sanghoon ; Crawford, Melba M.
Author_Institution
Dept. of Ind. Eng., Kyungwon Univ., Kyunggi-Do, South Korea
Volume
6
fYear
2001
fDate
2001
Firstpage
2887
Abstract
This study has utilized the approach to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. A region growing segmentation and local fuzzy classification have been employed to rind the sample classes that well represent the ground truth. The maximum likelihood classifier has then used the sample classes
Keywords
geophysical signal processing; geophysical techniques; image classification; image segmentation; remote sensing; terrain mapping; fuzzy training; geophysical measurement technique; image classification; image processing; image segmentation; land surface; maximum likelihood classifier; remote sensing; spatial region growing; terrain mapping; training; unsupervised classification; Cams; Clustering algorithms; Computational efficiency; Image processing; Image segmentation; Layout; Merging; Optical sensors; Partitioning algorithms; Pixel;
fLanguage
English
Publisher
ieee
Conference_Titel
Geoscience and Remote Sensing Symposium, 2001. IGARSS '01. IEEE 2001 International
Conference_Location
Sydney, NSW
Print_ISBN
0-7803-7031-7
Type
conf
DOI
10.1109/IGARSS.2001.978195
Filename
978195
Link To Document