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Open AccessArticle

Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images

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Key Laboratory of Subsurface Hydrology and Ecological Effects in Arid Region, Ministry of Education, School of Environmental Science and Engineering, Chang’an University, Xi’an 710054, China
2
The First Topographic Surveying Brigade of Shaanxi Bureau of Surveying and Mapping, Xi’an 710054, China
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School of Computer Science and Engineering, Xi’an University of Technology, Xi’an 710048, China
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Faculty of Electrical and Computer Engineering, University of Iceland, IS 107 Reykjavik, Iceland
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1238; https://doi.org/10.3390/rs10081238
Received: 6 July 2018 / Revised: 30 July 2018 / Accepted: 1 August 2018 / Published: 7 August 2018
(This article belongs to the Section Remote Sensing Image Processing)
Land cover classification that uses very high resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, the accuracy and usability of classification systems can still be improved. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed to improve the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image, and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. (1) An adaptive region is generated by gradually extending the region around a central pixel based on two predefined parameters (T1 and T2) to utilize the spatial feature of ground targets in a VHR image. (2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting. Each initial classified map is refined in this manner pixel by pixel. (3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated with two remote sensing images with high spatial resolutions of 1.0 m and 1.3 m. Compared with the classical majority voting method and a relatively new post-processing method called the general post-classification framework, the proposed D-AMVS can achieve a land cover classification map with less noise and higher classification accuracies. View Full-Text
Keywords: land cover classification; very high spatial resolution remote sensing image; adaptive majority vote; post-classification land cover classification; very high spatial resolution remote sensing image; adaptive majority vote; post-classification
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MDPI and ACS Style

Cui, G.; Lv, Z.; Li, G.; Atli Benediktsson, J.; Lu, Y. Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images. Remote Sens. 2018, 10, 1238.

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