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Article

Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery

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Faculty of Engineering, Department of Geomatics Engineering, Canakkale Onsekiz Mart University, 17100 Canakkale, Turkey
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Faculty of Architecture and Design, Department of Urban and Regional Planning, Land Use and Climate Change Laboratory, Canakkale Onsekiz Mart University, 17100 Canakkale, Turkey
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Photogrammetry Department, General Directorate of Mapping, 06100 Dikimevi Ankara, Turkey
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Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(11), 424; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110424
Received: 21 September 2018 / Revised: 13 October 2018 / Accepted: 27 October 2018 / Published: 31 October 2018
Using object-based image analysis (OBIA) techniques for land use-land cover classification (LULC) has become an area of interest due to the availability of high-resolution data and segmentation methods. Multi-resolution segmentation in particular, statistically seen as the most used algorithm, is able to produce non-identical segmentations depending on the required parameters. The total effect of segmentation parameters on the classification accuracy of high-resolution imagery is still an open question, though some studies were implemented to define the optimum segmentation parameters. However, recent studies have not properly considered the parameters and their consequences on LULC accuracy. The main objective of this study is to assess OBIA segmentation and classification accuracy according to the segmentation parameters using different overlap ratios during image object sampling for a predetermined scale. With this aim, we analyzed and compared (a) high-resolution color-infrared aerial images of a newly-developed urban area including different land use types; (b) combinations of multi-resolution segmentation with different shape, color, compactness, bands, and band-weights; and (c) accuracies of classifications based on varied segmentations. The results of various parameters in the study showed an explicit correlation between segmentation accuracies and classification accuracies. The effect of changes in segmentation parameters using different sample selection methods for five main LULC types was studied. Specifically, moderate shape and compactness values provided more consistency than lower and higher values; also, band weighting demonstrated substantial results due to the chosen bands. Differences in the variable importance of the classifications and changes in LULC maps were also explained. View Full-Text
Keywords: segmentation; object-based classification; orthophoto; land cover; high resolution imagery; infrared; accuracy segmentation; object-based classification; orthophoto; land cover; high resolution imagery; infrared; accuracy
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MDPI and ACS Style

Akcay, O.; Avsar, E.O.; Inalpulat, M.; Genc, L.; Cam, A. Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery. ISPRS Int. J. Geo-Inf. 2018, 7, 424. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110424

AMA Style

Akcay O, Avsar EO, Inalpulat M, Genc L, Cam A. Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery. ISPRS International Journal of Geo-Information. 2018; 7(11):424. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110424

Chicago/Turabian Style

Akcay, Ozgun, Emin O. Avsar, Melis Inalpulat, Levent Genc, and Ahmet Cam. 2018. "Assessment of Segmentation Parameters for Object-Based Land Cover Classification Using Color-Infrared Imagery" ISPRS International Journal of Geo-Information 7, no. 11: 424. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7110424

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