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Remote Sens. 2018, 10(2), 303;

Accuracy Assessment Measures for Object Extraction from Remote Sensing Images

School of Geography and Tourism, Qufu Normal University, Rizhao 276826, China
Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resource, Nanjing 210024, China
Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
School of Geosciences and Info-Physics, Central South University, Changsha 410012, China
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring, Central South University, Changsha 410012, China
School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China
Author to whom correspondence should be addressed.
Received: 18 November 2017 / Revised: 5 February 2018 / Accepted: 6 February 2018 / Published: 15 February 2018
(This article belongs to the Section Remote Sensing Image Processing)
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Object extraction from remote sensing images is critical for a wide range of applications, and object-oriented accuracy assessment plays a vital role in guaranteeing its quality. To evaluate object extraction accuracy, this paper presents several novel accuracy measures that differ from the norm. First, area-based and object number-based accuracy assessment measures are given based on a confusion matrix. Second, different accuracy assessment measures are provided by combining the similarities of multiple features. Third, to improve the reliability of the object extraction accuracy assessment results, two accuracy assessment measures based on object detail differences are designed. In contrast to existing measures, the presented method synergizes the feature similarity and distance difference, which considerably improves the reliability of object extraction evaluation. Encouraging results on two QuickBird images indicate the potential for further use of the presented algorithm. View Full-Text
Keywords: object-based image analysis; accuracy assessment; feature similarity; distance difference object-based image analysis; accuracy assessment; feature similarity; distance difference

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Cai, L.; Shi, W.; Miao, Z.; Hao, M. Accuracy Assessment Measures for Object Extraction from Remote Sensing Images. Remote Sens. 2018, 10, 303.

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