Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30
Land-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timel...
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Online Access: | https://www.mdpi.com/2072-4292/11/12/1397 |
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doaj-da8c447ef5ed4fe1b7e753f72607e2172020-11-25T01:55:15ZengMDPI AGRemote Sensing2072-42922019-06-011112139710.3390/rs11121397rs11121397Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30Cong Lin0Peijun Du1Alim Samat2Erzhu Li3Xin Wang4Junshi Xia5School of Geography and Ocean Science, Nanjing University, Nanjing 210093, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210093, ChinaState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Ürümqi 830011, ChinaSchool of Geography, Geomatics and Planning, Jiangsu Normal University, Xuzhou 221116, ChinaSchool of Geography and Ocean Science, Nanjing University, Nanjing 210093, ChinaGeoinformatics Unit, RIKEN Center for Advanced Intelligence Project, Tokyo 103-0027, JapanLand-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timely manner. However, conventional approaches always have the limitations of large amounts of sample collection and exploitation of relational knowledge between multi-modality remote sensing datasets. With some global land-cover products being available, it is important to produce new land-cover maps based on the existing land-cover products and time series images. To this end, a novel transfer learning based automatic approach was proposed for updating land cover maps of rapidly urbanizing regions. In detail, the proposed method is composed of the following three steps. The first is to design a strategy to extract reliable land-cover information from the historical land-cover map for one of the images (source domain). Then, a novel relational knowledge transfer technique is applied to transfer label information. Finally, classifiers are trained on the transferred samples with spatio-spectral features. The experimental results show that aforementioned steps can select sufficient effective samples for target images, and for the main land-cover classes in a rapidly urbanizing region; the results of an updated map show good performance in both precision and vision. Therefore, the proposed approach provides an automatic solution for urban land-cover mapping with a high degree of accuracy.https://www.mdpi.com/2072-4292/11/12/1397land-cover updatingrapidly urbanizing regionstransfer learningautomatic image classification |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Cong Lin Peijun Du Alim Samat Erzhu Li Xin Wang Junshi Xia |
spellingShingle |
Cong Lin Peijun Du Alim Samat Erzhu Li Xin Wang Junshi Xia Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30 Remote Sensing land-cover updating rapidly urbanizing regions transfer learning automatic image classification |
author_facet |
Cong Lin Peijun Du Alim Samat Erzhu Li Xin Wang Junshi Xia |
author_sort |
Cong Lin |
title |
Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30 |
title_short |
Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30 |
title_full |
Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30 |
title_fullStr |
Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30 |
title_full_unstemmed |
Automatic Updating of Land Cover Maps in Rapidly Urbanizing Regions by Relational Knowledge Transferring from GlobeLand30 |
title_sort |
automatic updating of land cover maps in rapidly urbanizing regions by relational knowledge transferring from globeland30 |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2019-06-01 |
description |
Land-cover map is the basis of research and application related to urban planning, environmental management and ecological protection. Land-cover updating is an essential task especially in a rapidly urbanizing region, where fast development makes it necessary to monitor land-cover change in a timely manner. However, conventional approaches always have the limitations of large amounts of sample collection and exploitation of relational knowledge between multi-modality remote sensing datasets. With some global land-cover products being available, it is important to produce new land-cover maps based on the existing land-cover products and time series images. To this end, a novel transfer learning based automatic approach was proposed for updating land cover maps of rapidly urbanizing regions. In detail, the proposed method is composed of the following three steps. The first is to design a strategy to extract reliable land-cover information from the historical land-cover map for one of the images (source domain). Then, a novel relational knowledge transfer technique is applied to transfer label information. Finally, classifiers are trained on the transferred samples with spatio-spectral features. The experimental results show that aforementioned steps can select sufficient effective samples for target images, and for the main land-cover classes in a rapidly urbanizing region; the results of an updated map show good performance in both precision and vision. Therefore, the proposed approach provides an automatic solution for urban land-cover mapping with a high degree of accuracy. |
topic |
land-cover updating rapidly urbanizing regions transfer learning automatic image classification |
url |
https://www.mdpi.com/2072-4292/11/12/1397 |
work_keys_str_mv |
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