• Adrijana Car – Thomas Jekel – Josef Strobl – Gerald Griesebner (Eds.)

GI_Forum 2018, Volume 6, 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

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.

Starting 2016, GI_Forum publishes two issues a Year.
Joumal Information is available at: GI-Forum
GI_Forum is listed on the Directory of Open Access Journals (DOAJ)

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GI_Forum 2018, Volume 6, Issue 2

ISSN 2308-1708
Online Edition

ISBN 978-3-7001-8447-8
Online Edition



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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: bestellung.verlag@oeaw.ac.at
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Creating Models of Custom Image Classification Workflows Using Softmax Regression and Support Vector Machine

    Jason Wolfe, Thomas Bahr, Xiaoying Jin

GI_Forum 2018, Volume 6, Issue 2, pp. 240-260, 2018/12/10

Journal for Geographic Information Science

doi: 10.1553/giscience2018_02_s240

doi: 10.1553/giscience2018_02_s240


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doi:10.1553/giscience2018_02_s240



doi:10.1553/giscience2018_02_s240

Abstract

This study demonstrates how complex image classification workflows can be built using a visual modelling tool. Models facilitate the comparison of different classifiers while allowing an analyst to experiment with different input features. The models include custom workflow steps for preparing input and training data, training the classifier, classifying images and evaluating the results. The example models presented here were used to classify Sentinel-2 imagery of eastern Texas, USA, into five land-use categories that consisted primarily of vegetation. Separate models were created for Softmax Regression and Support Vector Machine (SVM) classification, each using Sentinel-2 spectral bands and again with an additional entropy texture image as input. The results showed that SVM performed better than Softmax Regression and that the selected texture measure did not improve classification results. A discussion is provided of how the models could be extended further to provide different analysis options.

Keywords: classification, Softmax Regression, support vector machine, model, workflow, trainer

Published Online: 2018/12/10 12:48:53

Object Identifier: 0xc1aa5572 0x003a2031

Document viewed: Calculating...

Rights: https://creativecommons.org/licenses/by-nd/4.0/