Semi-automated training approaches for spectral class definition

P. V. Bolstad; T. M. Lillesand

doi:10.1080/01431169208904108

Semi-automated training approaches for spectral class definition

P. V. Bolstad, T. M. Lillesand

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Abstract

A semi-automated approach to spectral training for a maximum likelihood classification is shown to maintain or improve classification accuracy while reducing analyst input five-fold. The semi-automated approach is based on spectral sampling via region growing and training set refinement via transformed-divergence based mergers and deletions. Classification accuracies at the Anderson level II/1II in northern Wisconsin were higher for the semi-automated approach in five of six combinations of imagery, analyst, and study area, at significantly.

Original language	English (US)
Pages (from-to)	3157-3166
Number of pages	10
Journal	International Journal of Remote Sensing
Volume	13
Issue number	16
DOIs	https://doi.org/10.1080/01431169208904108
State	Published - Nov 1992

Bibliographical note

Funding Information:
Acknowledgments This work was supported by the University of Wisconsin Institute of Environmental Studies, the College of Agriculture and Life Sciences, and by Xi Sigma Pi. We also wish to thank M. Mohamed for his contributions to this research.

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author = "Bolstad, {P. V.} and Lillesand, {T. M.}",

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AU - Lillesand, T. M.

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AB - A semi-automated approach to spectral training for a maximum likelihood classification is shown to maintain or improve classification accuracy while reducing analyst input five-fold. The semi-automated approach is based on spectral sampling via region growing and training set refinement via transformed-divergence based mergers and deletions. Classification accuracies at the Anderson level II/1II in northern Wisconsin were higher for the semi-automated approach in five of six combinations of imagery, analyst, and study area, at significantly.

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Semi-automated training approaches for spectral class definition

Abstract

Bibliographical note

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