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4 December 1998 Compound deterministic pseudo-annealing Markov random field model for contextual classification of remotely sensed imagery
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Abstract
In this paper, we shall describe a new method for contextual classification of remotely sensed images. A compound deterministic pseudo annealing-Markov random field model that estimates the correct label for each pixel is suggested. The contextual information is used via the developed context function that depends on the probability function for the adjacent class labels. This function is then formulated as a discrete Markov Random Field (MRF). The global energy function to be minimized is made up of the adaptive a priori probability of classes and the context function. Without using the Metropolis criterion, the optimization procedure consists of selecting the new configuration that corresponds to the minimal value of the global energy function. We call such a procedure of optimization a Deterministic Pseudo Annealing (DPA). The method has been tested and evaluated on real multispectral image provided by the SPOT satellite. The results obtained have the same, or nearly the same, accuracy as those obtained with simulated annealing (SA)-based method and Iterated Conditional Modes (ICM)-based method. The convergence of the proposed DPA approach is better than SA method and very close to ICM method.
© (1998) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Salim Chitroub, Radja Khedam, H. Belhadj, and Boualem Sansal "Compound deterministic pseudo-annealing Markov random field model for contextual classification of remotely sensed imagery", Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); https://doi.org/10.1117/12.331891
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