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Proceedings Paper

Segmentation of LANDSAT TM image using the Markov random field model toward a category classification of higher accuracy
Author(s): Shuji Kawaguchi; Kensuke Yamazaki
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Paper Abstract

In category classification of remotely sensed imagery, it is important that pixels of image are classified using spatial informaton. We have implemented MRF(Markov Random Field) model for a classification of higher accuracy. The model of MRF is a random field whose random variable is owed to its neighborhood. The LANDSAT TM data of the Kanto area, Japan, has been alalyzed with the manner of iteration in which probability density function for a confiuration of classes reaches a maximum. Partly because of taking into account of edge information in image, the results show considerably good classification.

Paper Details

Date Published: 10 January 2005
PDF: 9 pages
Proc. SPIE 5657, Image Processing and Pattern Recognition in Remote Sensing II, (10 January 2005); doi: 10.1117/12.578405
Show Author Affiliations
Shuji Kawaguchi, Tokyo Gakugei Univ. (Japan)
Kensuke Yamazaki, Tokyo Gakugei Univ. (Japan)

Published in SPIE Proceedings Vol. 5657:
Image Processing and Pattern Recognition in Remote Sensing II
Yoshifumi Yasuoka; Stephen G. Ungar, Editor(s)

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