Image Classification and Land Cover Mapping Using Sentinel-2 Imagery: Optimization of SVM Parameters

作者：Saleh Yousefi, Somayeh Mirzaee, Hussein Almohamad, Ahmed Abdullah Al Dughairi, Christopher Gomez, Narges Siamian, Mona Alrasheedi, Hazem Ghassan Abdo · 发表于：Land · 年份：2022 · DOI：10.3390/land11070993 · 被引用次数：55 · 研究领域：Remote-Sensing Image Classification、Remote Sensing in Agriculture、Land Use and Ecosystem Services

Land use/cover (LU/LC) classification provides proxies of the natural and social processes related to urban development, providing stakeholders with crucial information. Remotely sensed images combined with supervised classification are common to define land use, but high-performance classifiers remain difficult to achieve, due to the presence of model hyperparameters. Conventional approaches rely on manual adjustment, which is time consuming and often unsatisfying. Therefore, the goal of this study has been to optimize the parameters of the support vector machine (SVM) algorithm for the generation of land use/cover maps from Sentinel-2 satellite imagery in selected humid and arid (three study sites each) climatic regions of Iran. For supervised SVM classification, we optimized two important parameters (gamma in kernel function and penalty parameter) of the LU/LC classification. Using the radial basis function (RBF) of the SVM classification method, we examined seven values for both parameters ranging from 0.001 to 1000. For both climate types, the penalty parameters (PP) showed a direct relationship with overall accuracy (OA). Statistical results confirmed that in humid study regions, LU/LC maps produced with a penalty parameter >100 were more accurate. However, for regions with arid climates, LU/LC maps with a penalty parameter >0.1 were more accurate. Mapping accuracy for both climate types was sensitive to the penalty parameter. In contrast, variations of the gamma ...