WIT Press


Classification Trees For Improving The Accuracy Of Land Use Urban Data From Remotely Sensed Images

Price

Free (open access)

Paper DOI

10.2495/MIS000361

Volume

24

Pages

11

Published

2000

Size

1,182 kb

Author(s)

M.T. Shalaby & A.A. Darwish

Abstract

This paper summarizes the results of a joint research carried out between the Informatics Institute of IT, University of Nottingham in the UK and the Information Technology Institute (ITI) in Egypt. The research focuses on achieving higher classification accuracy to extract urban planning data from remotely sensed images. The research aims at providing an affordable system, which is capable of operating with limited data to produce the best possible classification accuracy. A Landsat TM scene of Greater Cairo Metropolitan Area (GCMA) is used to test the validity and reliability of the proposed classification tree. The paper includes three main sections. The first introduces the current problem of the accuracy of urban data and the propo

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