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.

中文翻译：

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使用 Landsat 数据通过结合人工神经网络的 MLC、SAM 和 SVM 分类器的实施预测城市区域扩张

可靠的土地覆盖 (LC) 地图对于规划人员至关重要，因为缺少正确的土地覆盖地图可能会偏离项目。本研究的重点是使用三个众所周知的分类器和遥感数据进行土地覆盖分类和预测。最大似然分类器 (MLC)、光谱角映射器 (SAM) 和支持向量机 (SVM) 算法被用作参数、非参数和亚像素能力方法的代表，用于乌尔米亚市（伊朗）和它的郊区。2000 年、2010 年和 2020 年的 Landsat 图像已用于提供土地覆盖信息。结果表明，MLC 和 SVM 算法的总体准确度为 0.93–0.94，而 SAM 算法的总体准确度约为 0.79。MLC 的表现略好于 SVM 的分类器。使用元胞自动机人工神经网络方法预测土地覆盖变化。MLC 的总体精度高于其他精度，约为 0.94，尽管 SVM 对大面积段的精度稍高一些。对 2030 年的土地覆盖图进行了预测，表明该市将从 2000 年的 5500 公顷扩展到 2030 年的 9000 多公顷。