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Remote Sens. 2018, 10(1), 77; https://doi.org/10.3390/rs10010077

Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery

1
School of Earth Sciences and Engineering, Hohai University, Nanjing 210098, China
2
State Key Laboratory of Resources and Environmental Information System, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
3
School of Geography Science, Nanjing Normal University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Received: 24 November 2017 / Revised: 3 January 2018 / Accepted: 6 January 2018 / Published: 8 January 2018
(This article belongs to the Special Issue Uncertainty in Remote Sensing Image Analysis)
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Abstract

Pixel-based and object-based classifications are two commonly used approaches in extracting land cover information from remote sensing images. However, they each have their own inherent merits and limitations. This study, therefore, proposes a new classification method through the integration of pixel-based and object-based classifications (IPOC). Firstly, it employs pixel-based soft classification to obtain the class proportions of pixels to characterize the land cover details from pixel-scale properties. Secondly, it adopts area-to-point kriging to explore the class spatial dependence between objects for each pixel from object-based soft classification results. Thirdly, the class proportions of pixels and the class spatial dependence of pixels are fused as the class occurrence of pixels. Last, a linear optimization model on objects is built to determine the optimal class label of pixels within each object. Two remote sensing images are used to evaluate the effectiveness of IPOC. The experimental results demonstrate that IPOC performs better than the traditional pixel-based hard classification and object-based hard classification methods. Specifically, the overall accuracy of IPOC is 7.64% higher than that of pixel-based hard classification and 4.64% greater than that of object-based hard classification in the first experiment, while the overall accuracy improvements in the second experiment are 3.59% and 3.42%, respectively. Meanwhile, IPOC produces less salt and pepper effect than the pixel-based hard classification method and generates more accurate land cover details and small patches than the object-based hard classification method. View Full-Text
Keywords: land cover mapping; mixed object; uncertainty; pixel-based classification; object-based classification; integration land cover mapping; mixed object; uncertainty; pixel-based classification; object-based classification; integration
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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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Chen, Y.; Zhou, Y.; Ge, Y.; An, R.; Chen, Y. Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery. Remote Sens. 2018, 10, 77.

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