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Proceedings Paper

The use of thermodynamic quantities for improving multispectral class separation
Author(s): Dan Sheffer; Dov Ingman
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Paper Abstract

The present paper addresses the issue of extraction, processing, and recognition of information from multi-spectral observations of our surroundings. A new method of dealing with multispectral recognition problems is developed, in which a physical thermodynamic model is used to describe the properties of the object classes in a multispectral image of a certain scene. According to the model, different groups of objects in the image are canonical populations that are in thermodynamic equilibrium with each other and with their surroundings. Between the objects act forces that result from a potential field. Various thermodynamic properties of the populations are calculated. The difference between two populations is evaluated by first bringing them to a common temperature and then using the informational difference as a difference measure. The approach was implemented for a problem of combined formal and spectral classification of trees in a natural environment. The common temperature of two similar populations was varied until the separation between the populations reached a maximal value. A six-fold increase in the separation between the populations was achieved. In the future, we propose to use the Helmholtz free energy function as a quantity which attains a local minimum within each class of objects. An optimal classification scheme is one that minimizes the total free energy of the system.

Paper Details

Date Published: 1 June 2005
PDF: 10 pages
Proc. SPIE 5806, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI, (1 June 2005); doi: 10.1117/12.603094
Show Author Affiliations
Dan Sheffer, IARD--Institute for Advanced Research and Development (Israel)
Dov Ingman, Technion-Israel Institute of Technology (Israel)


Published in SPIE Proceedings Vol. 5806:
Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XI
Sylvia S. Shen; Paul E. Lewis, Editor(s)

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