An Evaluation of Classification Methods for Level II Land-Cover Categories in Ohio

摘要：

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.

作者: Robert C.Frohn Lin Liu Richard A.Beck Navendu Chaudhary Olimpia Arellano-Neri

作者单位: Department of Geography,University of Cincinnati,Cincinnati,Ohio 45221-0131

会议类型: 国际会议

会议名称: 第16届国际地理信息科学与技术大会(16th International Conference on GeoInformatics and the Joint Conference)

会议地点: 广州

会议语种:英文

在线出版日期: 2008-06-28（万方平台首次上网日期，不代表论文的发表时间）