Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification
Higher dimensionality, Hughes phenomenon, spatial resolution of image data, and presence of mixed pixels are the main challenges in a multi-spectral image classification process. Most of the classical machine learning algorithms suffer from scoring optimal classification performance over multi-spect...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-01-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/10/1/17 |
| _version_ | 1797476329318252544 |
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| author | Tagel Aboneh Abebe Rorissa Ramasamy Srinivasagan |
| author_facet | Tagel Aboneh Abebe Rorissa Ramasamy Srinivasagan |
| author_sort | Tagel Aboneh |
| collection | DOAJ |
| description | Higher dimensionality, Hughes phenomenon, spatial resolution of image data, and presence of mixed pixels are the main challenges in a multi-spectral image classification process. Most of the classical machine learning algorithms suffer from scoring optimal classification performance over multi-spectral image data. In this study, we propose stack-based ensemble-based learning approach to optimize image classification performance. In addition, we integrate the proposed ensemble learning with XGBoost method to further improve its classification accuracy. To conduct the experiment, the Landsat image data has been acquired from Bishoftu town located in the Oromia region of Ethiopia. The current study’s main objective was to assess the performance of land cover and land use analysis using multi-spectral image data. Results from our experiment indicate that, the proposed ensemble learning method outperforms any strong base classifiers with 99.96% classification performance accuracy. |
| first_indexed | 2024-03-09T20:56:20Z |
| format | Article |
| id | doaj.art-4f07a194fd9c49218f5e12a5c69bc5e2 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-09T20:56:20Z |
| publishDate | 2022-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-4f07a194fd9c49218f5e12a5c69bc5e22023-11-23T22:18:56ZengMDPI AGTechnologies2227-70802022-01-011011710.3390/technologies10010017Stacking-Based Ensemble Learning Method for Multi-Spectral Image ClassificationTagel Aboneh0Abebe Rorissa1Ramasamy Srinivasagan2Big Data and HPC Center of Excellence, Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, EthiopiaSchool of Information Sciences, College of Communication and Information, University of Tennessee (Knoxville), 1345 Circle Park Drive, 451 Communications Bldg, Knoxville, TN 37996, USABig Data and HPC Center of Excellence, Department of Software Engineering, Addis Ababa Science and Technology University, Addis Ababa P.O. Box 16417, EthiopiaHigher dimensionality, Hughes phenomenon, spatial resolution of image data, and presence of mixed pixels are the main challenges in a multi-spectral image classification process. Most of the classical machine learning algorithms suffer from scoring optimal classification performance over multi-spectral image data. In this study, we propose stack-based ensemble-based learning approach to optimize image classification performance. In addition, we integrate the proposed ensemble learning with XGBoost method to further improve its classification accuracy. To conduct the experiment, the Landsat image data has been acquired from Bishoftu town located in the Oromia region of Ethiopia. The current study’s main objective was to assess the performance of land cover and land use analysis using multi-spectral image data. Results from our experiment indicate that, the proposed ensemble learning method outperforms any strong base classifiers with 99.96% classification performance accuracy.https://www.mdpi.com/2227-7080/10/1/17multi-spectral image classificationensemble-based learningXGBoostingstacking method |
| spellingShingle | Tagel Aboneh Abebe Rorissa Ramasamy Srinivasagan Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification Technologies multi-spectral image classification ensemble-based learning XGBoosting stacking method |
| title | Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification |
| title_full | Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification |
| title_fullStr | Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification |
| title_full_unstemmed | Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification |
| title_short | Stacking-Based Ensemble Learning Method for Multi-Spectral Image Classification |
| title_sort | stacking based ensemble learning method for multi spectral image classification |
| topic | multi-spectral image classification ensemble-based learning XGBoosting stacking method |
| url | https://www.mdpi.com/2227-7080/10/1/17 |
| work_keys_str_mv | AT tagelaboneh stackingbasedensemblelearningmethodformultispectralimageclassification AT abeberorissa stackingbasedensemblelearningmethodformultispectralimageclassification AT ramasamysrinivasagan stackingbasedensemblelearningmethodformultispectralimageclassification |