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
This study demonstrates the use of a geographically weighted principal components analysis (GWPCA) of remote sensing imagery to improve land cover classification accuracy. A principal components analysis (PCA) is commonly applied in remote sensing but generates global, spatially-invariant results. GWPCA is a local adaptation of PCA that locally transforms the image data, and in doing so, can describe spatial change in the structure of the multi-band imagery, thus directly reflecting that many landscape processes are spatially heterogenic. In this research the GWPCA localised loadings of MODIS data are used as textural inputs, along with GWPCA localised ranked scores and the image bands themselves to three supervised classification algorithms. Using a reference data set for land cover to the west of Jakarta, Indonesia the classification procedure was assessed via training and validation data splits of 80/20, repeated 100 times. For each classification algorithm, the inclusion of the GWPCA loadings data was found to significantly improve classification accuracy. Further, but more moderate improvements in accuracy were found by additionally including GWPCA ranked scores as textural inputs, data that provide information on spatial anomalies in the imagery. The critical importance of considering both spatial structure and spatial anomalies of the imagery in the classification is discussed, together with the transferability of the new method to other studies. Research topics for method refinement are also suggested.
Metadata
Item Type: | Article |
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Authors/Creators: |
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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: |
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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: | 10.1016/j.isprsjprs.2016.06.014 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:103294 |