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Proceedings Paper

An evaluation of classification methods for level II land-cover categories in Ohio
Author(s): Robert C. Frohn; Lin Liu; Richard A. Beck; Navendu Chaudhary; Olimpia Arellano-Neri
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Paper Abstract

The purpose of this research was to evaluate six classifiers applied to Landsat-7 data for accuracy of Level II land-cover categories in Ohio. These methods consist of (1) USGS National Land Cover Data; (2) the spectral angle mapper; (3) the maximum likelihood classifier; (4) the maximum likelihood classifier with texture analysis; (5) a recently introduced hybrid artificial neural network; (6) and a recently introduced modified image segmentation and object-oriented processing classifier. The segmentation object-oriented processing (SOOP) classifier outperformed all others with an overall accuracy of 93.8% and Kappa Coefficient of 0.93. SOOP was the only classifier to have by-class producer and user accuracies of 90% or higher for all land-cover categories. A modified artificial neural network (ANN) classifier had the second highest overall accuracy of 87.6% and Kappa of 0.85. The four remaining classifiers had overall accuracies less than 85%. The SOOP classifier was applied to Landsat-7 data to perform a level II land-cover classification for the state of Ohio.

Paper Details

Date Published: 7 November 2008
PDF: 9 pages
Proc. SPIE 7147, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 71470D (7 November 2008); doi: 10.1117/12.813213
Show Author Affiliations
Robert C. Frohn, Univ. of Cincinnati (United States)
Lin Liu, Univ. of Cincinnati (United States)
Richard A. Beck, Univ. of Cincinnati (United States)
Navendu Chaudhary, Univ. of Cincinnati (United States)
Olimpia Arellano-Neri, Univ. of Cincinnati (United States)


Published in SPIE Proceedings Vol. 7147:
Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images
Lin Liu; Xia Li; Kai Liu; Xinchang Zhang, Editor(s)

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