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Proceedings Paper

Comparative study on multispectral agricultural image classification using Bayesian and neural network approaches
Author(s): Basel Solaiman; Marie-Catherine Mouchot; Ron J. Brown; Brian Brisco
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Paper Abstract

In this comparative study, the Bayesian and a neural network (the HLVQ) approach are used to classify multispectral LANDSAT images. The studied area contains several agricultural classes (wheat, flax,...). Some classes are found to be non homogeneous and thus are divided in this study into several subclasses. The Gaussian assumption needed by the Bayesian classifier is thus justified by this division. The main result obtained in this study is that the Bayesian classifier and the neural network considered here provide equivalent solutions for the classification of agricultural multispectral images.

Paper Details

Date Published: 31 January 1995
PDF: 8 pages
Proc. SPIE 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources, (31 January 1995); doi: 10.1117/12.200767
Show Author Affiliations
Basel Solaiman, Ecole Nationale Superieure des Telecommunications (France)
Marie-Catherine Mouchot, Ecole Nationale Superieure des Telecommunications (France)
Ron J. Brown, Canada Ctr. for Remote Sensing (Canada)
Brian Brisco, Canada Ctr. for Remote Sensing (Canada)


Published in SPIE Proceedings Vol. 2314:
Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources
Eric Mougin; K. Jon Ranson; James Alan Smith, Editor(s)

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