Evaluate the impact of empirical geometric correction models of satellite images on the land cover classification accuracy - نويره, فاطمه; عمر الخليل ,; إياد فحصة, (Latakia University Journal -Engineering Sciences Series, 2023)
Land cover classification is one of the important topics in the field of remote sensing. Satellite images are exposed to geometric distortions that affect the geometric quality of the spatial data extracted from them. Thus, they must be corrected by applying geometric correction. This research aims to evaluate the effects of the type of geometric correction models and resampling algorithms on the quality of the Maximum Likelihood-based land cover classification. 12 ground control points distributed uniformly were used for geometric correction. The research results showed that the nearest neighbor algorithm with the second-order polynomial model are the most suitable for the issue of land cover classification. In fact, overall accuracy of the classification reached 88.75% with a kappa coefficient of 0.85. On the other hand. The results showed that although the accuracy of the geometric correction using a rubber-sheeting model (±7.065m) was the best, the classification results were not. In this case, total accuracy value ranged between 59.493% and 60.7% and a kappa coefficient rages from 0.46 to 0.47, meaning that a more accurate geometric correction does not correspond to a more accurate classification result.