Urban land-use classification using machine learning classifiers: comparative evaluation and post-classification multi-feature fusion approach
| Autor: | Yashon O. Ouma, Amantle Keitsile, Boipuso Nkwae, Phillimon Odirile, Ditiro Moalafhi, Jiaguo Qi |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | European Journal of Remote Sensing, Vol 56, Iss 1 (2023) |
| Druh dokumentu: | article |
| ISSN: | 22797254 2279-7254 |
| DOI: | 10.1080/22797254.2023.2173659 |
| Popis: | ABSTRACTAccurate spatial-temporal mapping of urban land-use and land-cover (LULC) provides critical information for planning and management of urban environments. While several studies have investigated the significance of machine learning classifiers for urban land-use mapping, the determination of the optimal classifiers for the extraction of specific urban LULC classes in time and space is still a challenge especially for multitemporal and multisensor data sets. This study presents the results of urban LULC classification using decision tree-based classifiers comprising of gradient tree boosting (GTB), random forest (RF), in comparison with support vector machine (SVM) and multilayer perceptron neural networks (MLP-ANN). Using Landsat data from 1984 to 2020 at 5-year intervals for the Greater Gaborone Planning Area (GGPA) in Botswana, RF was the best classifier with overall average accuracy of 92.8%, MLP-ANN (91.2%), SVM (90.9%) and GTB (87.8%). To improve on the urban LULC mapping, the study presents a post-classification multiclass fusion of the best classifier results based on the principle of feature in-feature out (FEI-FEO) under mutual exclusivity boundary conditions. Through classifier ensemble, the FEI-FEO approach improved the overall LULC classification accuracy by more than 2% demonstrating the advantage of post-classification fusion in urban land-use mapping. |
| Databáze: | Directory of Open Access Journals |
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