期刊
ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION
卷 10, 期 8, 页码 -出版社
MDPI
DOI: 10.3390/ijgi10080513
关键词
urban expansion; change detection; change prediction; Support Vector Machines (SVMs); Maximum Likelihood Classifier (MLC); Spectral Angle Mapper (SAM); Artificial Neural Network
A reliable land cover map is crucial for planners, and this study focused on land cover classification and prediction using different classifiers in Urmia City and its suburbs. The study demonstrated high overall accuracies for MLC and SVMs algorithms, while SAM algorithm showed lower accuracy. The results also predicted land cover changes and expansion of the city from 5500 ha in 2000 to over 9000 ha in 2030.
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.
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