Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities
There are a variety of land cover products generated from remote-sensing images. However, misclassification errors in individual products and inconsistency among them undermine their utilities for research and other applications. While it is worth developing advanced pattern classifiers and utilizin...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/2/481 |
| _version_ | 1797437432638996480 |
|---|---|
| author | Wangle Zhang Jiwen Wang Hate Lin Ming Cong Yue Wan Jingxiong Zhang |
| author_facet | Wangle Zhang Jiwen Wang Hate Lin Ming Cong Yue Wan Jingxiong Zhang |
| author_sort | Wangle Zhang |
| collection | DOAJ |
| description | There are a variety of land cover products generated from remote-sensing images. However, misclassification errors in individual products and inconsistency among them undermine their utilities for research and other applications. While it is worth developing advanced pattern classifiers and utilizing the images of finer spatial, temporal, and/or spectral resolution for increased classification accuracy, it is also sensible to increase map classification accuracy through effective map fusion by exploiting complementarity among multi-source products over a study area. This paper presents a novel fusion method that works by weighting multiple source products based on their map-reference cover type transition probabilities, which are predicted using random forest for individual map pixels. The proposed method was tested and compared with three alternatives: consensus-based weighting, random forest, and locally modified Dempster–Shafer evidential reasoning, in a case study, over Shaanxi province, China. For this case study, three types of land cover products (GlobeLand30, FROM-GLC, and GLC_FCS30) of two nominal years (2010 and 2020) were used as the base maps for fusion. Reference sample data for model training and testing were collected following a robust stratified random sampling design that allows for augmenting reference data flexibly. Accuracy assessments show that overall accuracies (OAs) of fused land cover maps have been improved (1~9% in OAs), with the proposed method outperforming other methods by 2~8% in OAs. The proposed method does not need to have the base products’ classification systems harmonized beforehand, thus being robust and highly recommendable for fusing land cover products. |
| first_indexed | 2024-03-09T11:19:11Z |
| format | Article |
| id | doaj.art-70921eedf9b84f93a8034d56a46d8a9f |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:19:11Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-70921eedf9b84f93a8034d56a46d8a9f2023-12-01T00:22:18ZengMDPI AGRemote Sensing2072-42922023-01-0115248110.3390/rs15020481Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition ProbabilitiesWangle Zhang0Jiwen Wang1Hate Lin2Ming Cong3Yue Wan4Jingxiong Zhang5College of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaCollege of Geological Engineering and Geomatics, Chang’an University, Xi’an 710054, ChinaCollege of Resource and Environment, Henan Agricultural University, Zhengzhou 450002, ChinaSchool of Geodesy and Geomatics, Wuhan University, Wuhan 430079, ChinaThere are a variety of land cover products generated from remote-sensing images. However, misclassification errors in individual products and inconsistency among them undermine their utilities for research and other applications. While it is worth developing advanced pattern classifiers and utilizing the images of finer spatial, temporal, and/or spectral resolution for increased classification accuracy, it is also sensible to increase map classification accuracy through effective map fusion by exploiting complementarity among multi-source products over a study area. This paper presents a novel fusion method that works by weighting multiple source products based on their map-reference cover type transition probabilities, which are predicted using random forest for individual map pixels. The proposed method was tested and compared with three alternatives: consensus-based weighting, random forest, and locally modified Dempster–Shafer evidential reasoning, in a case study, over Shaanxi province, China. For this case study, three types of land cover products (GlobeLand30, FROM-GLC, and GLC_FCS30) of two nominal years (2010 and 2020) were used as the base maps for fusion. Reference sample data for model training and testing were collected following a robust stratified random sampling design that allows for augmenting reference data flexibly. Accuracy assessments show that overall accuracies (OAs) of fused land cover maps have been improved (1~9% in OAs), with the proposed method outperforming other methods by 2~8% in OAs. The proposed method does not need to have the base products’ classification systems harmonized beforehand, thus being robust and highly recommendable for fusing land cover products.https://www.mdpi.com/2072-4292/15/2/481land coverfusiontransition probabilityerror matrixaugmented samplingaccuracy |
| spellingShingle | Wangle Zhang Jiwen Wang Hate Lin Ming Cong Yue Wan Jingxiong Zhang Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities Remote Sensing land cover fusion transition probability error matrix augmented sampling accuracy |
| title | Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities |
| title_full | Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities |
| title_fullStr | Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities |
| title_full_unstemmed | Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities |
| title_short | Fusing Multiple Land Cover Products Based on Locally Estimated Map-Reference Cover Type Transition Probabilities |
| title_sort | fusing multiple land cover products based on locally estimated map reference cover type transition probabilities |
| topic | land cover fusion transition probability error matrix augmented sampling accuracy |
| url | https://www.mdpi.com/2072-4292/15/2/481 |
| work_keys_str_mv | AT wanglezhang fusingmultiplelandcoverproductsbasedonlocallyestimatedmapreferencecovertypetransitionprobabilities AT jiwenwang fusingmultiplelandcoverproductsbasedonlocallyestimatedmapreferencecovertypetransitionprobabilities AT hatelin fusingmultiplelandcoverproductsbasedonlocallyestimatedmapreferencecovertypetransitionprobabilities AT mingcong fusingmultiplelandcoverproductsbasedonlocallyestimatedmapreferencecovertypetransitionprobabilities AT yuewan fusingmultiplelandcoverproductsbasedonlocallyestimatedmapreferencecovertypetransitionprobabilities AT jingxiongzhang fusingmultiplelandcoverproductsbasedonlocallyestimatedmapreferencecovertypetransitionprobabilities |