Drone-based land-cover mapping using a fuzzy unordered rule induction algorithm integrated into object-based image analysis

Kalantar, B; Mansor, SB; Sameen, MI; Pradhan, B; Shafri, HZM

Drone-based land-cover mapping using a fuzzy unordered rule induction algorithm integrated into object-based image analysis

Kalantar, B Mansor, SB Sameen, MI Pradhan, B

Shafri, HZM

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Publication Type:: Journal Article
Citation:: International Journal of Remote Sensing, 2017, 38 (8-10), pp. 2535 - 2556
Issue Date:: 2017-05-19

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Field	Value	Language
dc.contributor.author	Kalantar, B	en_US
dc.contributor.author	Mansor, SB	en_US
dc.contributor.author	Sameen, MI	en_US
dc.contributor.author	Pradhan, B https://orcid.org/0000-0001-9863-2054	en_US
dc.contributor.author	Shafri, HZM	en_US
dc.date.issued	2017-05-19	en_US
dc.identifier.citation	International Journal of Remote Sensing, 2017, 38 (8-10), pp. 2535 - 2556	en_US
dc.identifier.issn	0143-1161	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123498
dc.description.abstract	© 2017 Informa UK Limited, trading as Taylor & Francis Group. Land-cover maps provide essential data for a wide range of practical and small-scale applications. A number of data sources appropriate for land-cover extraction are available. Among these, images captured using unmanned aerial vehicles (UAVs) are low cost, have very high resolution, and can be acquired at any time with few restrictions. Over the past two decades, various classification techniques have been developed to extract land-cover features from UAV images, and object-based image analysis (OBIA) is the preferred technique based on the recent literature. This study presents a novel method that integrates the fuzzy unordered rule induction algorithm (FURIA) into OBIA to achieve accurate land-cover extraction from UAV images. The images were segmented using a multiresolution segmentation algorithm with an optimized scale parameter. The scale parameter was optimized using a novel approach that integrated feature space optimization into the plateau objective function. During the classification stage, significant features were selected via random forest, and rule sets were developed using FURIA. For comparison, result of the proposed approach was compared with those of decision tree (DT) rules and the Support Vector Machine (SVM) classification method. The results of this study indicate that the proposed method outperforms DT and SVM with an overall accuracy of 91.23%. A transferability evaluation showed that FURIA achieved accurate classification results on different UAV image subsets captured at different times. The findings suggest that fuzzy rules are more appropriate than conventional crisp rules for land-cover extraction from UAV images.	en_US
dc.relation.ispartof	International Journal of Remote Sensing	en_US
dc.relation.isbasedon	10.1080/01431161.2016.1277043	en_US
dc.subject.classification	Geological & Geomatics Engineering	en_US
dc.title	Drone-based land-cover mapping using a fuzzy unordered rule induction algorithm integrated into object-based image analysis	en_US
dc.type	Journal Article
utslib.citation.volume	8-10	en_US
utslib.citation.volume	38	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0909 Geomatic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
pubs.organisational-group	/University of Technology Sydney/Strength - CAMGIS - Centre for Advanced Modelling and Geospatial lnformation Systems
utslib.copyright.status	closed_access
pubs.issue	8-10	en_US
pubs.publication-status	Published	en_US
pubs.volume	38	en_US

Abstract:

© 2017 Informa UK Limited, trading as Taylor & Francis Group. Land-cover maps provide essential data for a wide range of practical and small-scale applications. A number of data sources appropriate for land-cover extraction are available. Among these, images captured using unmanned aerial vehicles (UAVs) are low cost, have very high resolution, and can be acquired at any time with few restrictions. Over the past two decades, various classification techniques have been developed to extract land-cover features from UAV images, and object-based image analysis (OBIA) is the preferred technique based on the recent literature. This study presents a novel method that integrates the fuzzy unordered rule induction algorithm (FURIA) into OBIA to achieve accurate land-cover extraction from UAV images. The images were segmented using a multiresolution segmentation algorithm with an optimized scale parameter. The scale parameter was optimized using a novel approach that integrated feature space optimization into the plateau objective function. During the classification stage, significant features were selected via random forest, and rule sets were developed using FURIA. For comparison, result of the proposed approach was compared with those of decision tree (DT) rules and the Support Vector Machine (SVM) classification method. The results of this study indicate that the proposed method outperforms DT and SVM with an overall accuracy of 91.23%. A transferability evaluation showed that FURIA achieved accurate classification results on different UAV image subsets captured at different times. The findings suggest that fuzzy rules are more appropriate than conventional crisp rules for land-cover extraction from UAV images.

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http://hdl.handle.net/10453/123498