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Proceedings Paper

Vegetation classification method with spectral, spatial, and temporal variability for Landsat/TM imagery
Author(s): Dikdik Setia Permana; Takanori Nakajima; Tetsuya Yuasa; Takao Akatsuka
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Paper Abstract

A vegetation classification model, which takes account of not only spectral information of data but also spatial and temporal information, is proposed for high spatial resolution multispectral scanner data such as Landsat/TM (thematic mapper) images. For this purpose, Markov random field model (MRF) is introduced for spectral, spatial and temporal information of data. The MRF exploits spatial class dependencies between neighboring pixels in any image and temporal class dependencies between temporal sequences. By integrating spectral, spatial, and temporal information in the classification model, it is expected to improve classification accuracy. The performance of the proposed model is investigated by using actual Landsat/TM temporal images. The experimental results shows that the classification accuracy of proposed model is about 5.09% higher than Maximum Likelihood Method that used as reference model. From this experiment, we can conclude that the proposed model is useful for classification of Landsat/TM images.

Paper Details

Date Published: 1 October 1998
PDF: 10 pages
Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); doi: 10.1117/12.323244
Show Author Affiliations
Dikdik Setia Permana, Yamagata Univ. (Japan)
Takanori Nakajima, Yamagata Univ. (Japan)
Tetsuya Yuasa, Yamagata Univ. (Japan)
Takao Akatsuka, Yamagata Univ. (Japan)


Published in SPIE Proceedings Vol. 3460:
Applications of Digital Image Processing XXI
Andrew G. Tescher, Editor(s)

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