Prediction of Urban Area Expansion with Implementation of MLC, SAM and SVMs’ Classifiers Incorporating Artificial Neural Network Using Landsat Data
| Autor: | Seyed-Mohammad Tavakkoli-Sabour, Saeid Zare Naghadehi, John van Genderen, Samira-Sadat Saleh, Milad Asadi, Mohammad Maleki |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Artificial Neural Network
010504 meteorology & atmospheric sciences Computer science Geography Planning and Development 0211 other engineering and technologies 02 engineering and technology Land cover Urban area 01 natural sciences Support Vector Machines (SVMs) Classifier (linguistics) Earth and Planetary Sciences (miscellaneous) Computers in Earth Sciences change detection 021101 geological & geomatics engineering 0105 earth and related environmental sciences Parametric statistics Geography (General) geography geography.geographical_feature_category Artificial neural network business.industry Pattern recognition Maximum Likelihood Classifier (MLC) Spectral Angle Mapper (SAM) Subpixel rendering Support vector machine G1-922 Artificial intelligence change prediction business Change detection urban expansion |
| Zdroj: | ISPRS International Journal of Geo-Information Volume 10 Issue 8 ISPRS International Journal of Geo-Information, Vol 10, Iss 513, p 513 (2021) |
| ISSN: | 2220-9964 |
| DOI: | 10.3390/ijgi10080513 |
| Popis: | A reliable land cover (LC) map is essential for planners, as missing proper land cover maps may deviate a project. This study is focusing on land cover classification and prediction using three well known classifiers and remote sensing data. Maximum Likelihood classifier (MLC), Spectral Angle Mapper (SAM), and Support Vector Machines (SVMs) algorithms are used as the representatives for parametric, non-parametric and subpixel capable methods for change detection and change prediction of Urmia City (Iran) and its suburbs. Landsat images of 2000, 2010, and 2020 have been used to provide land cover information. The results demonstrated 0.93–0.94 overall accuracies for MLC and SVMs’ algorithms, but it was around 0.79 for the SAM algorithm. The MLC performed slightly better than SVMs’ classifier. Cellular Automata Artificial neural network method was used to predict land cover changes. Overall accuracy of MLC was higher than others at about 0.94 accuracy, although, SVMs were slightly more accurate for large area segments. Land cover maps were predicted for 2030, which demonstrate the city’s expansion from 5500 ha in 2000 to more than 9000 ha in 2030. |
| Databáze: | OpenAIRE |
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