Improving land cover classification using input variables derived from a geographically weighted principal components analysis

Comber, AJ, Harris, P and Tsutsumida, N (2016) Improving land cover classification using input variables derived from a geographically weighted principal components analysis. ISPRS Journal of Photogrammetry and Remote Sensing, 119. pp. 347-360. ISSN 0924-2716

Abstract

Metadata

Authors/Creators:
  • Comber, AJ
  • Harris, P
  • Tsutsumida, N
Copyright, Publisher and Additional Information: © 2016, Published by Elsevier B.V. on behalf of International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). This is an author produced version of a paper published in ISPRS Journal of Photogrammetry and Remote Sensing. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: GWmodel; GWPCA; Spatial heterogeneity; Accuracy
Dates:
  • Published: September 2016
  • Accepted: 17 June 2016
  • Published (online): 19 July 2016
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Environment (Leeds) > School of Geography (Leeds) > Centre for Spatial Analysis & Policy (Leeds)
Depositing User: Symplectic Publications
Date Deposited: 05 Aug 2016 10:16
Last Modified: 19 Jul 2017 14:39
Published Version: http://dx.doi.org/10.1016/j.isprsjprs.2016.06.014
Status: Published
Publisher: Elsevier
Identification Number: https://doi.org/10.1016/j.isprsjprs.2016.06.014

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