Abstract
An image processing algorithm to process remotely sensed data is presented based on use of Bayesian decision making, Gibbs random fields, and stochastic relaxation. The compound Bayesian decision rule is used for estimation of maximal marginal a posteriori probability of map region labels, Gibbs random fields are used to model pair-wise interaction between picture elements, and stochastic relaxation is used to solve the maximum likelihood function of interaction potentials. Images are assumed piece-wise homogeneous. The addition of texture attributes for region helps convergence during stochastic relaxation. The specialized, and context dependent features are extracted automatically. Application of the algorithm is given for the Siberian Altai, Lake Baikal, and Lake Teletskoye in Russia.
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© 2002 Springer Science+Business Media Dordrecht
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Kovalevskaya, N.M. (2002). Landscape Indication Based on Stochastic Relaxation. In: Muttiah, R.S. (eds) From Laboratory Spectroscopy to Remotely Sensed Spectra of Terrestrial Ecosystems. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-1620-8_6
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DOI: https://doi.org/10.1007/978-94-017-1620-8_6
Publisher Name: Springer, Dordrecht
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