Integration of Ant Colony Optimization and Object-Based Analysis for LiDAR Data Classification

Sameen, MI; Pradhan, B; Shafri, HZM; Mezaal, MR; Hamid, HB

Integration of Ant Colony Optimization and Object-Based Analysis for LiDAR Data Classification

Sameen, MI Pradhan, B

Shafri, HZM Mezaal, MR Hamid, HB

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Publication Type:: Journal Article
Citation:: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10 (5), pp. 2055 - 2066
Issue Date:: 2017-05-01

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Field	Value	Language
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.contributor.author	Mezaal, MR	en_US
dc.contributor.author	Hamid, HB	en_US
dc.date.issued	2017-05-01	en_US
dc.identifier.citation	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017, 10 (5), pp. 2055 - 2066	en_US
dc.identifier.issn	1939-1404	en_US
dc.identifier.uri	http://hdl.handle.net/10453/123506
dc.description.abstract	© 2017 IEEE. Light detection and ranging (LiDAR) data classification provides useful thematic maps for numerous geospatial applications. Several methods and algorithms have been proposed recently for LiDAR data classification. Most studies focused on object-based analysis because of its advantages over per-pixel-based methods. However, several issues, such as parameter optimization, attribute selection, and development of transferable rulesets, remain challenging in this topic. This study contributes to LiDAR data classification by developing an approach that integrates ant colony optimization (ACO) and rule-based classification. First, LiDAR-derived digital elevation and digital surface models were integrated with high-resolution orthophotos. Second, the processed raster was segmented with the multiresolution segmentation method. Subsequently, the parameters were optimized with a supervised technique based on fuzzy analysis. A total of 20 attributes were selected based on general knowledge on the study area and LiDAR data; the best subset containing 12 attributes was then selected via ACO. These attributes were utilized to develop rulesets through the use of a decision tree algorithm, and a thematic map was generated for the study area. Results revealed the robustness of the proposed method, which has an overall accuracy of ∼95% and a kappa coefficient of 0.94. The rule-based approach with all attributes and the k nearest neighbor (KNN) classification method were applied to validate the results of the proposed method. The overall accuracy of the rule-based method with all attributes was ∼88% (kappa = 0.82), whereas the KNN method had an overall accuracy of <70% and produced a poor thematic map. The selection of the ACO algorithm was justified through a comparison with three well-known feature selection methods. On the other hand, the transferability of the developed rules was evaluated by using a second LiDAR dataset at another study area. The overall accuracy and the kappa index for the second study area were 92% and 0.90, respectively. Overall, the findings indicate that the selection of a subset with significant attributes is important for accurate LiDAR data classification with object-based methods.	en_US
dc.relation.ispartof	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing	en_US
dc.relation.isbasedon	10.1109/JSTARS.2017.2650956	en_US
dc.title	Integration of Ant Colony Optimization and Object-Based Analysis for LiDAR Data Classification	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	10	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0406 Physical Geography and Environmental Geoscience	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	10	en_US

Abstract:

© 2017 IEEE. Light detection and ranging (LiDAR) data classification provides useful thematic maps for numerous geospatial applications. Several methods and algorithms have been proposed recently for LiDAR data classification. Most studies focused on object-based analysis because of its advantages over per-pixel-based methods. However, several issues, such as parameter optimization, attribute selection, and development of transferable rulesets, remain challenging in this topic. This study contributes to LiDAR data classification by developing an approach that integrates ant colony optimization (ACO) and rule-based classification. First, LiDAR-derived digital elevation and digital surface models were integrated with high-resolution orthophotos. Second, the processed raster was segmented with the multiresolution segmentation method. Subsequently, the parameters were optimized with a supervised technique based on fuzzy analysis. A total of 20 attributes were selected based on general knowledge on the study area and LiDAR data; the best subset containing 12 attributes was then selected via ACO. These attributes were utilized to develop rulesets through the use of a decision tree algorithm, and a thematic map was generated for the study area. Results revealed the robustness of the proposed method, which has an overall accuracy of ∼95% and a kappa coefficient of 0.94. The rule-based approach with all attributes and the k nearest neighbor (KNN) classification method were applied to validate the results of the proposed method. The overall accuracy of the rule-based method with all attributes was ∼88% (kappa = 0.82), whereas the KNN method had an overall accuracy of <70% and produced a poor thematic map. The selection of the ACO algorithm was justified through a comparison with three well-known feature selection methods. On the other hand, the transferability of the developed rules was evaluated by using a second LiDAR dataset at another study area. The overall accuracy and the kappa index for the second study area were 92% and 0.90, respectively. Overall, the findings indicate that the selection of a subset with significant attributes is important for accurate LiDAR data classification with object-based methods.

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