GI_Forum 2020, Volume 8, Issue 2 Journal for Geographic Information Science
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |
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DATUM, UNTERSCHRIFT / DATE, SIGNATURE
BANK AUSTRIA CREDITANSTALT, WIEN (IBAN AT04 1100 0006 2280 0100, BIC BKAUATWW), DEUTSCHE BANK MÜNCHEN (IBAN DE16 7007 0024 0238 8270 00, BIC DEUTDEDBMUC)
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GI_Forum 2020, Volume 8, Issue 2 Journal for Geographic Information Science
ISSN 2308-1708 Online Edition ISBN 978-3-7001-8858-2 Online Edition
Chi-Feng Yen,
Douglas Alan Stow,
Sory Toure
S. 160 - 174 doi:10.1553/giscience2020_02_s160 Verlag der Österreichischen Akademie der Wissenschaften doi:10.1553/giscience2020_02_s160
Abstract: Geographic object-based image analysis (GEOBIA) is commonly applied for land-cover and land-use mapping, updating and change-identification analyses. Following image segmentation, conventional GEOBIA routines classify image objects based on parametric statistical measures, assuming that within-object pixels have normally distributed image brightness signatures. The context for this study is updating extant land-use GIS layers that are out of date as a result of urban expansion. The objective is to develop, test and compare GEOBIA techniques based on a histogram classifier and on a nearest-neighbour classifier, for updating land-use layers. Frequency distribution signatures of land-use change and no-change objects are evaluated for different feature inputs and classifiers within an urbanizing area in San Diego County, California, USA. The results demonstrate that a histogram classifier consistently outperforms a conventional nearest-neighbour classifier. A Histogram Matching Root Sum Squared Differential Area classifier combined with temporal-spectral difference inputs and arithmetic mean for combining multi-feature classifier metrics yielded the greatest accuracy: 79.82% overall accuracy, with 78.72% and 81.07% for change and no-change objects respectively. Keywords: land-use change identification, urban growth, GEOBIA, histogram matching Published Online: 2020/12/15 12:23:53 Object Identifier: 0xc1aa5576 0x003c1403 Rights:https://creativecommons.org/licenses/by-nd/4.0/
GI_Forum publishes high quality original research across the transdisciplinary field of Geographic Information Science (GIScience). The journal provides a platform for dialogue among GI-Scientists and educators, technologists and critical thinkers in an ongoing effort to advance the field and ultimately contribute to the creation of an informed GISociety. Submissions concentrate on innovation in education, science, methodology and technologies in the spatial domain and their role towards a more just, ethical and sustainable science and society. GI_Forum implements the policy of open access publication after a double-blind peer review process through a highly international team of seasoned scientists for quality assurance. Special emphasis is put on actively supporting young scientists through formative reviews of their submissions. Only English language contributions are published.
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Verlag der Österreichischen Akademie der Wissenschaften Austrian Academy of Sciences Press
A-1011 Wien, Dr. Ignaz Seipel-Platz 2
Tel. +43-1-515 81/DW 3420, Fax +43-1-515 81/DW 3400 https://verlag.oeaw.ac.at, e-mail: verlag@oeaw.ac.at |