File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Developing Long Time Series 1-km Land Cover Maps from 5-km AVHRR Data Using a Super-Resolution Method

TitleDeveloping Long Time Series 1-km Land Cover Maps from 5-km AVHRR Data Using a Super-Resolution Method
Authors
KeywordsFocal loss temporal convolutional long short-term memory (FL-T-ConvLSTM)
Land cover (LC)
Long time series
Quantitative remote sensing parameters
Super-resolution
Issue Date2021
Citation
IEEE Transactions on Geoscience and Remote Sensing, 2021, v. 59, n. 7, p. 5479-5493 How to Cite?
AbstractDynamic land cover (LC) information is an essential part of environmental and ecological research. Therefore, acquiring dynamic LC data with high spatial resolution has attracted a great deal of attention in the remote sensing community. Nevertheless, the high-temporal resolution satellite data tend to have a coarse spatial resolution, and satellite data with high temporal resolution are often relatively low. Obtaining LC with high spatiotemporal resolution is extremely challenging. The super-resolution method can help researchers achieve this goal, and the recently developed neural-network-based deep learning algorithms have great potential for use as an alternative solution. This study proposes a focal loss temporal convolutional long short-term memory (FL-T-ConvLSTM) model for super-resolution LC classification research. It first trains the deep FL-T-ConvLSTM network to establish a transformation between low-resolution quantitative remote sensing parameters and high-resolution quantitative remote sensing parameters and then engages in nonlinear mapping with a high-resolution LC map. A long-term series 1-km super-resolution LC classification model based on deep learning was established and applied to the Beijing-Tianjin-Hebei region. Based on this method, a long-term series of 1-km LC maps from 1982 to 2019 can be obtained. The test accuracy and field validation accuracy of the model reached 90.1% and 86.8% when using reliable test samples and field test samples, respectively. This study provides a method for obtaining high-resolution LC classification products from low-resolution quantitative remote-sensing products.
Persistent Identifierhttp://hdl.handle.net/10722/323131
ISSN
2023 Impact Factor: 7.5
2023 SCImago Journal Rankings: 2.403
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorWang, Haoyu-
dc.contributor.authorZhao, Xiang-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorWu, Donghai-
dc.contributor.authorZhang, Xin-
dc.contributor.authorWang, Qian-
dc.contributor.authorZhao, Jiacheng-
dc.contributor.authorDu, Xiaozheng-
dc.contributor.authorZhou, Qian-
dc.date.accessioned2022-11-18T11:54:57Z-
dc.date.available2022-11-18T11:54:57Z-
dc.date.issued2021-
dc.identifier.citationIEEE Transactions on Geoscience and Remote Sensing, 2021, v. 59, n. 7, p. 5479-5493-
dc.identifier.issn0196-2892-
dc.identifier.urihttp://hdl.handle.net/10722/323131-
dc.description.abstractDynamic land cover (LC) information is an essential part of environmental and ecological research. Therefore, acquiring dynamic LC data with high spatial resolution has attracted a great deal of attention in the remote sensing community. Nevertheless, the high-temporal resolution satellite data tend to have a coarse spatial resolution, and satellite data with high temporal resolution are often relatively low. Obtaining LC with high spatiotemporal resolution is extremely challenging. The super-resolution method can help researchers achieve this goal, and the recently developed neural-network-based deep learning algorithms have great potential for use as an alternative solution. This study proposes a focal loss temporal convolutional long short-term memory (FL-T-ConvLSTM) model for super-resolution LC classification research. It first trains the deep FL-T-ConvLSTM network to establish a transformation between low-resolution quantitative remote sensing parameters and high-resolution quantitative remote sensing parameters and then engages in nonlinear mapping with a high-resolution LC map. A long-term series 1-km super-resolution LC classification model based on deep learning was established and applied to the Beijing-Tianjin-Hebei region. Based on this method, a long-term series of 1-km LC maps from 1982 to 2019 can be obtained. The test accuracy and field validation accuracy of the model reached 90.1% and 86.8% when using reliable test samples and field test samples, respectively. This study provides a method for obtaining high-resolution LC classification products from low-resolution quantitative remote-sensing products.-
dc.languageeng-
dc.relation.ispartofIEEE Transactions on Geoscience and Remote Sensing-
dc.subjectFocal loss temporal convolutional long short-term memory (FL-T-ConvLSTM)-
dc.subjectLand cover (LC)-
dc.subjectLong time series-
dc.subjectQuantitative remote sensing parameters-
dc.subjectSuper-resolution-
dc.titleDeveloping Long Time Series 1-km Land Cover Maps from 5-km AVHRR Data Using a Super-Resolution Method-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TGRS.2020.3018109-
dc.identifier.scopuseid_2-s2.0-85112283765-
dc.identifier.volume59-
dc.identifier.issue7-
dc.identifier.spage5479-
dc.identifier.epage5493-
dc.identifier.eissn1558-0644-
dc.identifier.isiWOS:000665167500008-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats