21 September 1994 Spatial pattern classification for optical agricultural remote sensing
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We describe a new method for computing approximations to the marginal probability mass function of the random variables in a Markov random field (MRF). When applied to the a posteriori MRF, this yields approximations to the conditional marginal probability mass function, which is the key quantity in a Bayesian classifier. We apply these ideas to an optical agricultural remote sensing problem where they outperform the pixel-by-pixel ML classifier by 38%.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Chi-hsin Wu, Chi-hsin Wu, Peter C. Doerschuk, Peter C. Doerschuk, } "Spatial pattern classification for optical agricultural remote sensing", Proc. SPIE 2298, Applications of Digital Image Processing XVII, (21 September 1994); doi: 10.1117/12.186567; https://doi.org/10.1117/12.186567

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