Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105395
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Land Surveying and Geo-Informatics | - |
dc.creator | Chen, P | - |
dc.creator | Huang, H | - |
dc.creator | Shi, W | - |
dc.creator | Chen, R | - |
dc.date.accessioned | 2024-04-12T06:52:11Z | - |
dc.date.available | 2024-04-12T06:52:11Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/105395 | - |
dc.language.iso | en | en_US |
dc.publisher | Molecular Diversity Preservation International (MDPI) | en_US |
dc.rights | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | en_US |
dc.rights | The following publication Chen, P.; Huang, H.; Shi, W.; Chen, R. A Reference-Free Method for the Thematic Accuracy Estimation of Global Land Cover Products Based on the Triple Collocation Approach. Remote Sens. 2023, 15, 2255 is available at https://doi.org/10.3390/rs15092255. | en_US |
dc.subject | Accuracy estimation | en_US |
dc.subject | Land cover | en_US |
dc.subject | Local classification strategy | en_US |
dc.subject | Reference-free method | en_US |
dc.subject | Triple collocation approach (TCCA) | en_US |
dc.title | A reference-free method for the thematic accuracy estimation of global land cover products based on the triple collocation approach | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.volume | 15 | - |
dc.identifier.issue | 9 | - |
dc.identifier.doi | 10.3390/rs15092255 | - |
dcterms.abstract | Global land cover (GLC) data are an indispensable resource for understanding the relationship between human activities and the natural environment. Estimating their classification accuracy is significant for studying environmental change and sustainable development. With the rapid emergence of various GLC products, the lack of high-quality reference data poses a severe risk to traditional accuracy estimation methods, in which reference data are always required. Thus, meeting the needs of large-scale, fast evaluation for GLC products becomes challenging. The triple collocation approach (TCCA) is originally applied to assess classification accuracy in earthquake damage mapping when ground truth is unavailable. TCCA can provide unbiased accuracy estimation of three classification systems when their errors are conditionally independent. In this study, we extend the idea of TCCA and test its performance in the accuracy estimation of GLC data without ground reference data. Firstly, to generate two additional classification systems besides the original GLC data, a k-order neighbourhood is defined for each assessment unit (i.e., geographic tiles), and a local classification strategy is implemented to train two classifiers based on local samples and features from remote sensing images. Secondly, to reduce the uncertainty from complex classification schemes, the multi-class problem in GLC is transformed into multiple binary-class problems when estimating the accuracy of each land class. Building upon over 15 million sample points with remote sensing features retrieved from Google Earth Engine, we demonstrate the performance of our method on WorldCover 2020, and the experiment shows that screening reliable sample points during training local classifiers can significantly improve the overall estimation with a relative error of less than 4% at the continent level. This study proves the feasibility of estimating GLC accuracy using the existing land information and remote sensing data, reducing the demand for costly reference data in GLC assessment and enriching the assessment approaches for large-scale land cover data. | - |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Remote sensing, May 2023, v. 15, no. 9, 2255 | - |
dcterms.isPartOf | Remote sensing | - |
dcterms.issued | 2023-05 | - |
dc.identifier.scopus | 2-s2.0-85159314886 | - |
dc.identifier.eissn | 2072-4292 | - |
dc.identifier.artn | 2255 | - |
dc.description.validate | 202403 bcvc | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_Scopus/WOS | en_US |
dc.description.fundingSource | others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; Guangdong Basic and Applied Basic Research Foundation | 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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remotesensing-15-02255.pdf | 4.29 MB | Adobe PDF | View/Open |
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