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Remote Sens. 2019, 11(1), 88; https://doi.org/10.3390/rs11010088

Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas

1
School of Geodesy and Geomatics, Wuhan University, Wuhan 430072, China
2
Electronic Information School, Wuhan University, Wuhan 430072, China
*
Author to whom correspondence should be addressed.
Received: 8 November 2018 / Revised: 25 December 2018 / Accepted: 28 December 2018 / Published: 7 January 2019
(This article belongs to the Special Issue Citizen Science and Earth Observation II)
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Abstract

Land cover classification of urban areas is critical for understanding the urban environment. High-resolution remotely sensed imagery provides abundant, detailed spatial information for urban classification. In the meantime, OpenStreetMap (OSM) data, as typical crowd-sourced geographical information, have been an emerging data source for obtaining urban information. In this context, a land cover classification method that fuses high-resolution remotely sensed imagery and OSM data is proposed. Training samples were generated by integrating the OSM data and multiple information indexes. OSM data, which contain class attributes and location information of urban objects, served as the labels of initial training samples. Multiple information indexes that reflect spectral and spatial characteristics of different classes were utilized to improve the training set. Morphological attribute profiles were used because the structural and contextual information of images was effective in distinguishing the classes with similar spectral characteristics. Moreover, a road superimposition strategy that considers road hierarchy was developed because OSM data provide road information with high completeness in the urban area. Experiments were conducted on the data captured over Wuhan city, and three state-of-the-art approaches were adopted for comparison. Results show that the proposed approach obtains satisfactory results and outperforms the other comparative approaches. View Full-Text
Keywords: high-resolution remotely sensed imagery; OpenStreetMap; classification; sample collection; spatial information; road hierarchy high-resolution remotely sensed imagery; OpenStreetMap; classification; sample collection; spatial information; road hierarchy
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Luo, N.; Wan, T.; Hao, H.; Lu, Q. Fusing High-Spatial-Resolution Remotely Sensed Imagery and OpenStreetMap Data for Land Cover Classification Over Urban Areas. Remote Sens. 2019, 11, 88.

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