The application of artificial neural networks to the analysis of remotely sensed data

Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural...

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Bibliographic Details
Published in:International journal of remote sensing 2008-02, Vol.29 (3), p.617-663
Main Authors: Mas, J. F., Flores, J. J.
Format: Article
Language:English
Subjects:
Applied geophysics
Earth sciences
Earth, ocean, space
Exact sciences and technology
Internal geophysics
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Summary:Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis.
ISSN:0143-1161
1366-5901
DOI:10.1080/01431160701352154