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Proceedings Paper

Compound deterministic pseudo-annealing Markov random field model for contextual classification of remotely sensed imagery
Author(s): Salim Chitroub; Radja Khedam; H. Belhadj; Boualem Sansal
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Paper 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.

Paper Details

Date Published: 4 December 1998
PDF: 12 pages
Proc. SPIE 3500, Image and Signal Processing for Remote Sensing IV, (4 December 1998); doi: 10.1117/12.331891
Show Author Affiliations
Salim Chitroub, Univ. of Science and Technology Houari Boumedienne (Algeria)
Radja Khedam, Univ. of Science and Technology Houari Boumedienne (Algeria)
H. Belhadj, Univ. of Science and Technology Houari Boumedienne (Algeria)
Boualem Sansal, Univ. of Science and Technology Houari Boumedienne (Algeria)


Published in SPIE Proceedings Vol. 3500:
Image and Signal Processing for Remote Sensing IV
Sebastiano Bruno Serpico, Editor(s)

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