30 October 2009 A spatial-temporal contextual classification approach based on Markov random fields using multi-temporal imagery
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Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961I (2009) https://doi.org/10.1117/12.832474
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
Abstract
The traditional classification method based on the spectral information in pixel level faces the problem of spectrum confusion. Pixels on multi-temporal image show dependencies in both the spatial and temporal domains besides spectral information. When spectral information has limited discriminative power, spatial-temporal dependencies can help to remove the spectral confusion. Two ETM+ images in different seasons after processing are used and supervised classification algorithm-the maximum likelihood classification (MLC) is used to initialize the algorithm proposed in this article. Then a Markov Random Fields (MRF) model is used to model the spatial-temporal contextual prior probabilities of images. Lastly the likelihood estimates of spectral observation from MLC and conditional spatial-temporal priors from MRF are integrated into posterior estimates by Bayes rule, the optimal classification was achieved when the classification corresponds to maximum a posteriori (MAP). The results show that MRF is an efficient probabilistic model for analysis of spatial and temporal contextual information. A spatial-temporal classification algorithm that explicitly integrates spectral, spatial and temporal information in multi-temporal images can achieve significant improvements over non-contextual classification. Some errors have been avoided because of the integration of space and time information.
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Wei Fu, Wei Fu, Ziqi Guo, Ziqi Guo, Qiang Zhou, Qiang Zhou, } "A spatial-temporal contextual classification approach based on Markov random fields using multi-temporal imagery", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961I (30 October 2009); doi: 10.1117/12.832474; https://doi.org/10.1117/12.832474
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