Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/107503
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.contributor | Research Institute for Land and Space | - |
dc.creator | Li, Z | en_US |
dc.creator | Weng, Q | en_US |
dc.creator | Zhou, Y | en_US |
dc.creator | Dou, P | en_US |
dc.creator | Ding, X | en_US |
dc.date.accessioned | 2024-06-27T07:29:45Z | - |
dc.date.available | 2024-06-27T07:29:45Z | - |
dc.identifier.issn | 0034-4257 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/107503 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier BV | en_US |
dc.rights | © 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/). | en_US |
dc.rights | The following publication Li, Z., Weng, Q., Zhou, Y., Dou, P., & Ding, X. (2024). Learning spectral-indices-fused deep models for time-series land use and land cover mapping in cloud-prone areas: The case of Pearl River Delta. Remote Sensing of Environment, 308, 114190 is available at https://doi.org/10.1016/j.rse.2024.114190. | en_US |
dc.subject | Cloudy and rainy areas | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Land use and land cover | en_US |
dc.subject | Sentinel-2 | en_US |
dc.subject | Time series images | en_US |
dc.title | Learning spectral-indices-fused deep models for time-series land use and land cover mapping in cloud-prone areas : the case of Pearl River Delta | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 308 | en_US |
dc.identifier.doi | 10.1016/j.rse.2024.114190 | en_US |
dcterms.abstract | Mapping of highly dynamic changes in land use and land cover (LULC) can be hindered by various cloudy conditions with optical satellite images. These conditions result in discontinuities in high-temporal-density LULC mapping. In this paper, we developed an integrated time series mapping method to enhance the LULC mapping accuracy and frequency in cloud-prone areas by incorporating spectral-indices-fused deep models and time series reconstruction techniques. The proposed method first reconstructed cloud-contaminated pixels through time series filtering, during which the cloud masks initialized by a deep model were refined and updated during the reconstruction process. Then, the reconstructed time series images were fed into a spectral-indices-fused deep model trained on samples collected worldwide for classification. Finally, post-classification processing, including spatio-temporal majority filtering and time series refinement considering land–water interactions, was conducted to enhance the LULC mapping accuracy and consistency. We applied the proposed method to the cloud- and rain-prone Pearl River Delta (i.e., Guangdong–Hong Kong–Macao Greater Bay Area, GBA) and used time series Sentinel-2 images as the experimental data. The proposed method enabled seamless LULC mapping at a temporal frequency of 2–5 days, and the production of 10 m resolution annual LULC products in the GBA. The assessment yielded a mean overall accuracy of 87.01% for annual mapping in the four consecutive years of 2019–2022 and outperformed existing mainstream LULC products, including ESA WorldCover (83.98%), Esri Land Cover (85.26%), and Google Dynamic World (85.06%). Our assessment also reveals significant variations in LULC mapping accuracies with different cloud masks, thus underscoring their critical role in time series LULC mapping. The proposed method has the potential to generate seamless and near real-time maps for other regions in the world by using deep models trained on datasets collected globally. This method can provide high-quality LULC data sets at different time intervals for various land and water dynamics in cloud- and rain-prone regions. Notwithstanding the difficulties of obtaining high-quality LULC maps in cloud-prone areas, this paper provides a novel approach for the mapping of LULC dynamics and the provision of reliable annual LULC products. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Remote sensing of environment, 1 July 2024, v. 308, 114190 | en_US |
dcterms.isPartOf | Remote sensing of environment | en_US |
dcterms.issued | 2024-07-01 | - |
dc.identifier.scopus | 2-s2.0-85191991296 | - |
dc.identifier.eissn | 1879-0704 | en_US |
dc.identifier.artn | 114190 | en_US |
dc.description.validate | 202406 bcch | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | a2901a | - |
dc.identifier.SubFormID | 48689 | - |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | Global STEM Professorship by the Hong Kong SAR Government; The Hong Kong Polytechnic University Start-up Fund; Institute of Land and Space at The Hong Kong Polytechnic University; National Natural Science Foundation of China (No. 42101357) | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
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File | Description | Size | Format | |
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1-s2.0-S0034425724002086-main.pdf | 21.69 MB | Adobe PDF | View/Open |
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