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Open AccessFeature PaperArticle

Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes

Pegasus Technical Services, Inc., c/o U.S. Environmental Protection Agency, Cincinnati, OH 45219, USA
U.S. Environmental Protection Agency, Office of Research and Development, Cincinnati, OH 45268, USA
Department of Geography, Binghamton University, State University of New York, Binghamton, NY 13902, USA
Institute of General and Experimental Biology SB RAS, 670047 Ulan-Ude, Russia
V.B. Sochava Institute of Geography SB RAS, 664033 Irkutsk, Russia
Irkutsk State University, 664003 Irkutsk, Russia
Department of Geography, University of Cincinnati, Cincinnati, OH 45220, USA
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(1), 46;
Received: 11 October 2017 / Revised: 11 December 2017 / Accepted: 20 December 2017 / Published: 28 December 2017
(This article belongs to the Special Issue Remote Sensing of Floodpath Lakes and Wetlands)
Wetland ecosystems straddle both terrestrial and aquatic habitats, performing many ecological functions directly and indirectly benefitting humans. However, global wetland losses are substantial. Satellite remote sensing and classification informs wise wetland management and monitoring. Both pixel- and object-based classification approaches using parametric and non-parametric algorithms may be effectively used in describing wetland structure and habitat, but which approach should one select? We conducted both pixel- and object-based image analyses (OBIA) using parametric (Iterative Self-Organizing Data Analysis Technique, ISODATA, and maximum likelihood, ML) and non-parametric (random forest, RF) approaches in the Barguzin Valley, a large wetland (~500 km2) in the Lake Baikal, Russia, drainage basin. Four Quickbird multispectral bands plus various spatial and spectral metrics (e.g., texture, Non-Differentiated Vegetation Index, slope, aspect, etc.) were analyzed using field-based regions of interest sampled to characterize an initial 18 ISODATA-based classes. Parsimoniously using a three-layer stack (Quickbird band 3, water ratio index (WRI), and mean texture) in the analyses resulted in the highest accuracy, 87.9% with pixel-based RF, followed by OBIA RF (segmentation scale 5, 84.6% overall accuracy), followed by pixel-based ML (83.9% overall accuracy). Increasing the predictors from three to five by adding Quickbird bands 2 and 4 decreased the pixel-based overall accuracy while increasing the OBIA RF accuracy to 90.4%. However, McNemar’s chi-square test confirmed no statistically significant difference in overall accuracy among the classifiers (pixel-based ML, RF, or object-based RF) for either the three- or five-layer analyses. Although potentially useful in some circumstances, the OBIA approach requires substantial resources and user input (such as segmentation scale selection—which was found to substantially affect overall accuracy). Hence, we conclude that pixel-based RF approaches are likely satisfactory for classifying wetland-dominated landscapes. View Full-Text
Keywords: Lake Baikal; maximum likelihood; near-infrared; Quickbird; random forest; segmentation Lake Baikal; maximum likelihood; near-infrared; Quickbird; random forest; segmentation
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Berhane, T.M.; Lane, C.R.; Wu, Q.; Anenkhonov, O.A.; Chepinoga, V.V.; Autrey, B.C.; Liu, H. Comparing Pixel- and Object-Based Approaches in Effectively Classifying Wetland-Dominated Landscapes. Remote Sens. 2018, 10, 46.

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