Segmentation of LANDSAT TM image using the Markov random field model toward a category classification of higher accuracy

Shuji Kawaguchi; Kensuke Yamazaki

doi:10.1117/12.578405

10 January 2005 Segmentation of LANDSAT TM image using the Markov random field model toward a category classification of higher accuracy

Shuji Kawaguchi, Kensuke Yamazaki

Author Affiliations +

Proceedings Volume 5657, Image Processing and Pattern Recognition in Remote Sensing II; (2005) https://doi.org/10.1117/12.578405
Event: Fourth International Asia-Pacific Environmental Remote Sensing Symposium 2004: Remote Sensing of the Atmosphere, Ocean, Environment, and Space, 2004, Honolulu, Hawai'i, United States

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.

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Shuji Kawaguchi and Kensuke Yamazaki "Segmentation of LANDSAT TM image using the Markov random field model toward a category classification of higher accuracy", Proc. SPIE 5657, Image Processing and Pattern Recognition in Remote Sensing II, (10 January 2005); https://doi.org/10.1117/12.578405

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KEYWORDS

Image segmentation

Earth observing sensors

Landsat

Image classification

Magnetorheological finishing

Data modeling

Data processing

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