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Proceedings Paper

Selective detection of linear features in geological remote sensing data
Author(s): Jo Ann Parikh; John S. DaPonte; Emily G. DiNicola; Robert A. Pedersen
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Paper Abstract

One of the major problems in the development of computer-assisted systems for geologic mapping is how to individualize the system to meet user needs. Ideally, the system should be responsive to specifications of desired types of output structures. Also, the system should be able to incorporate the user's knowledge of regional characteristics into the feature extraction/selection and classification components. Automatic techniques for classification of remote sensing data typically require relatively large, labeled training sets which are well- organized with respect to the desired mapping between input and output patterns. The present paper focuses on the feature extraction/selection component of the system. Kohonen self- organizing feature maps in conjunction with image processing procedures for linear feature extraction are used for explorative data analysis, feature selection, and construction of exemplar patterns. The results of training Kohonen feature maps with different pattern sets and different feature combinations provide insight into the nature of pattern relationships which enables the user to develop sets of positive and negative training patterns for the classification component.

Paper Details

Date Published: 16 September 1992
PDF: 10 pages
Proc. SPIE 1709, Applications of Artificial Neural Networks III, (16 September 1992); doi: 10.1117/12.139973
Show Author Affiliations
Jo Ann Parikh, Southern Connecticut State Univ. (United States)
John S. DaPonte, Southern Connecticut State Univ. (United States)
Emily G. DiNicola, Southern Connecticut State Univ. (United States)
Robert A. Pedersen, Southern Connecticut State Univ. (United States)


Published in SPIE Proceedings Vol. 1709:
Applications of Artificial Neural Networks III
Steven K. Rogers, Editor(s)

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