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Open AccessArticle

Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity

1
Department of Land Survey and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
2
Hubei Soil and Water Conservation Engineering Research Center, Hubei Water Resources Research Institute, Wuhan 430070, China
3
School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(21), 2509; https://doi.org/10.3390/rs11212509
Received: 24 September 2019 / Revised: 17 October 2019 / Accepted: 24 October 2019 / Published: 26 October 2019
(This article belongs to the Special Issue Multitemporal Land Cover and Land Use Mapping)
Land use/cover (LUC) data are commonly relied on to provide land surface information in a variety of applications. However, the exchange and joint use of LUC information from different datasets can be challenging due to semantic differences between common classification systems (CSs). In this paper, we propose an uncertainty assessment schema to capture the semantic translation uncertainty between heterogeneous LUC CSs and evaluate the data label uncertainty of multitemporal LUC mapping results caused by uncertainty propagation. The semantic translation uncertainty between CSs is investigated using a dynamic semantic reference system (DSRS) model and semantic similarity analysis. An object-based unsupervised change detection algorithm is adopted to determine the probability of changes in land patches, and novel uncertainty metrics are proposed to estimate the patch label uncertainty in LUC maps. The proposed uncertainty assessment schema was validated via experiments on four LUC datasets, and the results confirmed that semantic uncertainty had great impact on data reliability and that the uncertainty metrics could be used in the development of uncertainty controls in multitemporal LUC mapping by referring to uncertainty assessment results. We anticipate our findings will be used to improve the applicability and interoperability of LUC data products. View Full-Text
Keywords: land use/cover mapping; classification system; semantic uncertainty; uncertainty analysis; change detection land use/cover mapping; classification system; semantic uncertainty; uncertainty analysis; change detection
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MDPI and ACS Style

Zhang, X.; Shi, W.; Lv, Z. Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity. Remote Sens. 2019, 11, 2509. https://doi.org/10.3390/rs11212509

AMA Style

Zhang X, Shi W, Lv Z. Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity. Remote Sensing. 2019; 11(21):2509. https://doi.org/10.3390/rs11212509

Chicago/Turabian Style

Zhang, Xiaokang; Shi, Wenzhong; Lv, Zhiyong. 2019. "Uncertainty Assessment in Multitemporal Land Use/Cover Mapping with Classification System Semantic Heterogeneity" Remote Sens. 11, no. 21: 2509. https://doi.org/10.3390/rs11212509

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