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Open AccessEditorial

Special Section Guest Editorial: Change Detection Using Multi-Source Remotely Sensed Imagery

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School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
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The State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
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Fondazione Bruno Kessler, Università degli Studi di Trento, 38122 Trento Area, Italy
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Center for OPTical IMagery Analysis and Learning (OPTIMAL), Northwestern Polytechnical University, P.O. Box 64, 127 West Youyi Road, Xi’an 710072, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2216; https://doi.org/10.3390/rs11192216
Received: 18 September 2019 / Accepted: 20 September 2019 / Published: 23 September 2019
(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)
This special issue hosts papers on change detection technologies and analysis in remote sensing, including multi-source sensors, advanced machine learning technologies for change information mining, and the utilization of these technologies in a variety of geospatial applications. The presented results showed improved results when multi-source remote sensed data was used in change detection. View Full-Text
Keywords: change detection; multi-source remote sensing; deep learning; multi-scale; image segmentation change detection; multi-source remote sensing; deep learning; multi-scale; image segmentation
MDPI and ACS Style

Huang, X.; Li, J.; Bovolo, F.; Wang, Q. Special Section Guest Editorial: Change Detection Using Multi-Source Remotely Sensed Imagery. Remote Sens. 2019, 11, 2216.

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