Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data

Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data

Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectral bands over decades are most desirable for this...

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Bibliographic Details
Main Authors: Haobo Lyu, Hui Lu, Lichao Mou, Wenyu Li, Jonathon Wright, Xuecao Li, Xinlu Li, Xiao Xiang Zhu, Jie Wang, Le Yu, Peng Gong
Format: Article
Language:English
Published: MDPI AG 2018-03-01
Series:Remote Sensing
Subjects:
urban mapping
deep learning
recurrent neural network
transfer learning
long time series
Online Access:http://www.mdpi.com/2072-4292/10/3/471

Internet

http://www.mdpi.com/2072-4292/10/3/471

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