Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data
The Tree-based Pipeline Optimization Tool (TPOT) is a state-of-the-art automated machine learning (AutoML) approach that automatically generates and optimizes tree-based pipelines using a genetic algorithm. Although it has been proven to outperform commonly used machine techniques, its capability to...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/14/7/1687 |
| _version_ | 1797437817748455424 |
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| author | Zolo Kiala John Odindi Onisimo Mutanga |
| author_facet | Zolo Kiala John Odindi Onisimo Mutanga |
| author_sort | Zolo Kiala |
| collection | DOAJ |
| description | The Tree-based Pipeline Optimization Tool (TPOT) is a state-of-the-art automated machine learning (AutoML) approach that automatically generates and optimizes tree-based pipelines using a genetic algorithm. Although it has been proven to outperform commonly used machine techniques, its capability to handle high-dimensional datasets has not been investigated. In vegetation mapping and analysis, multi-date images are generally high-dimensional datasets that contain embedded information, such as phenological and canopy structural properties, known to enhance mapping accuracy. However, without the implementation of a robust classification algorithm or a feature selection tool, the large sets and the presence of redundant variables in multi-date images can impede accurate and efficient landscape classification. Hence, this study sought to test the efficacy of the TPOT on a multi-date Sentinel-2 image to optimize the classification accuracies of a landscape infested by a noxious invasive plant species, the parthenium weed (<i>Parthenium hysterophorus</i>). Specifically, the models created from the multi-date image, using the TPOT and an algorithm system that combines feature selection and the TPOT, dubbed “ReliefF-Svmb-EXT-TPOT”, were compared. The results showed that the TPOT could perform well on data with large feature sets, but at a computational cost. The overall accuracies were 91.9% and 92.6% using the TPOT and ReliefF-Svmb-EXT-TPOT models, respectively. The study findings are crucial for automated and accurate mapping of parthenium weed using high-dimensional geospatial datasets with limited human intervention. |
| first_indexed | 2024-03-09T11:28:07Z |
| format | Article |
| id | doaj.art-038b6e72d33948928b589f9dd2977bb3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:28:07Z |
| publishDate | 2022-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-038b6e72d33948928b589f9dd2977bb32023-11-30T23:57:31ZengMDPI AGRemote Sensing2072-42922022-03-01147168710.3390/rs14071687Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image DataZolo Kiala0John Odindi1Onisimo Mutanga2Discipline of Geography and Environmental Science, School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3201, South AfricaDiscipline of Geography and Environmental Science, School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3201, South AfricaDiscipline of Geography and Environmental Science, School of Agricultural Earth and Environmental Sciences, University of KwaZulu-Natal, Private Bag X01, Scottsville, Pietermaritzburg 3201, South AfricaThe Tree-based Pipeline Optimization Tool (TPOT) is a state-of-the-art automated machine learning (AutoML) approach that automatically generates and optimizes tree-based pipelines using a genetic algorithm. Although it has been proven to outperform commonly used machine techniques, its capability to handle high-dimensional datasets has not been investigated. In vegetation mapping and analysis, multi-date images are generally high-dimensional datasets that contain embedded information, such as phenological and canopy structural properties, known to enhance mapping accuracy. However, without the implementation of a robust classification algorithm or a feature selection tool, the large sets and the presence of redundant variables in multi-date images can impede accurate and efficient landscape classification. Hence, this study sought to test the efficacy of the TPOT on a multi-date Sentinel-2 image to optimize the classification accuracies of a landscape infested by a noxious invasive plant species, the parthenium weed (<i>Parthenium hysterophorus</i>). Specifically, the models created from the multi-date image, using the TPOT and an algorithm system that combines feature selection and the TPOT, dubbed “ReliefF-Svmb-EXT-TPOT”, were compared. The results showed that the TPOT could perform well on data with large feature sets, but at a computational cost. The overall accuracies were 91.9% and 92.6% using the TPOT and ReliefF-Svmb-EXT-TPOT models, respectively. The study findings are crucial for automated and accurate mapping of parthenium weed using high-dimensional geospatial datasets with limited human intervention.https://www.mdpi.com/2072-4292/14/7/1687parthenium weedmulti-date imagesingle-datehybrid feature selection methodTPOT |
| spellingShingle | Zolo Kiala John Odindi Onisimo Mutanga Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data Remote Sensing parthenium weed multi-date image single-date hybrid feature selection method TPOT |
| title | Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data |
| title_full | Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data |
| title_fullStr | Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data |
| title_full_unstemmed | Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data |
| title_short | Determining the Capability of the Tree-Based Pipeline Optimization Tool (TPOT) in Mapping Parthenium Weed Using Multi-Date Sentinel-2 Image Data |
| title_sort | determining the capability of the tree based pipeline optimization tool tpot in mapping parthenium weed using multi date sentinel 2 image data |
| topic | parthenium weed multi-date image single-date hybrid feature selection method TPOT |
| url | https://www.mdpi.com/2072-4292/14/7/1687 |
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