Comparative study on multispectral agricultural image classification using Bayesian and neural network approaches

Basel Solaiman; Marie-Catherine Mouchot; Ron J. Brown; Brian Brisco

doi:10.1117/12.200767

31 January 1995 Comparative study on multispectral agricultural image classification using Bayesian and neural network approaches

Basel Solaiman, Marie-Catherine Mouchot, Ron J. Brown, Brian Brisco

Author Affiliations +

Proceedings Volume 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources; (1995) https://doi.org/10.1117/12.200767
Event: Satellite Remote Sensing, 1994, Rome, Italy

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.

Citation Download Citation

Basel Solaiman, Marie-Catherine Mouchot, Ron J. Brown, and Brian Brisco "Comparative study on multispectral agricultural image classification using Bayesian and neural network approaches", Proc. SPIE 2314, Multispectral and Microwave Sensing of Forestry, Hydrology, and Natural Resources, (31 January 1995); https://doi.org/10.1117/12.200767

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