Land use land cover mapping using advanced machine learning classifiers
| dc.contributor.author | Jamali, A. | |
| dc.date.accessioned | 2024-09-29T16:16:22Z | |
| dc.date.available | 2024-09-29T16:16:22Z | |
| dc.date.issued | 2021 | |
| dc.department | Karabük Üniversitesi | en_US |
| dc.description.abstract | Due to the recent climate changes such as floods and droughts, there is a need for Land Use Land Cover (LULC) mapping to monitor environmental changes that have effects on ecology, policy management, health and disaster management. As such, in this study, two well-known machine learning classifiers, namely, Support Vector Machine (SVM) and Random Forest (RF), are used for land cover mapping. In addition, two advanced deep learning algorithms, namely, the GAMLP and FSMLP, that are based on the Multi-layer Perceptron (MLP) function are developed in MATLAB programming language. The GAMLP uses a Genetic Algorithm (GA) to optimise parameters of the MLP function and, on the other hand, the FSMLP uses a derivative-free function for optimisation of the MLP function parameters. Three different scenarios using Landsat-8 imagery with spatial resolutions of 30 and 15 m are defined to investigate the effects of data pre-processing on the final predicted LULC map. Results based on the statistical indices, including overall accuracy (OA) and kappa index, show that the developed MLP-based algorithms have relatively high accuracies with higher than 98% correct classification. Besides the statistical indices, final LULC maps are interpreted visually where the GAMLP and FSMLP give the best results for the pre-processed Landsat-8 imagery with a spatial resolution of 15 m, but they have the worst outcomes for the unprocessed Landsat-8 imagery compared to SVM and RF classifiers visually and statistically. © 2021 Ali Jamali, published by Sciendo. | en_US |
| dc.identifier.doi | 10.2478/eko-2021-0031 | |
| dc.identifier.endpage | 300 | en_US |
| dc.identifier.issn | 1335-342X | |
| dc.identifier.issue | 3 | en_US |
| dc.identifier.scopus | 2-s2.0-85118787635 | en_US |
| dc.identifier.scopusquality | Q3 | en_US |
| dc.identifier.startpage | 286 | en_US |
| dc.identifier.uri | https://doi.org/10.2478/eko-2021-0031 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.14619/9052 | |
| dc.identifier.volume | 40 | en_US |
| dc.indekslendigikaynak | Scopus | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Sciendo | en_US |
| dc.relation.ispartof | Ekologia Bratislava | en_US |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | image classification | en_US |
| dc.subject | LULC | en_US |
| dc.subject | machine learning | en_US |
| dc.subject | Multi-layer Perceptron | en_US |
| dc.subject | Random Forest | en_US |
| dc.subject | Support Vector Machine | en_US |
| dc.title | Land use land cover mapping using advanced machine learning classifiers | en_US |
| dc.type | Article | en_US |
