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...

Full description

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

Similar Items

Similar Items