Vegetation classification method with spectral, spatial, and temporal variability for Landsat/TM imagery

Dikdik Setia Permana; Takanori Nakajima; Tetsuya Yuasa; Takao Akatsuka

doi:10.1117/12.323244

1 October 1998 Vegetation classification method with spectral, spatial, and temporal variability for Landsat/TM imagery

Dikdik Setia Permana, Takanori Nakajima, Tetsuya Yuasa, Takao Akatsuka

Proceedings Volume 3460, Applications of Digital Image Processing XXI; (1998) https://doi.org/10.1117/12.323244
Event: SPIE's International Symposium on Optical Science, Engineering, and Instrumentation, 1998, San Diego, CA, United States

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.

Citation Download Citation

Dikdik Setia Permana, Takanori Nakajima, Tetsuya Yuasa, and Takao Akatsuka "Vegetation classification method with spectral, spatial, and temporal variability for Landsat/TM imagery", Proc. SPIE 3460, Applications of Digital Image Processing XXI, (1 October 1998); https://doi.org/10.1117/12.323244

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