COMPARISON OF PIXEL AND OBJECT BASED CLASSIFICATION METHODS ON RAPIDEYE SATELLITE IMAGE

Abstract

The aim of this study is to evaluate the classification performances of land use/land cover (LULC) classification methods by comparing the results of pixel and object-based classification approaches on RapidEye satellite image. Pixel-based classification was carried out in ERDAS Imagine 10.4 using the Maximum Likelihood-supervised approach, whilst object-based classification was performed in e-Cognition Developer 64 using the nearest neighbour-supervised classification method. A LULC map of eight classes was created in both methods. While the accuracy for thematic LULC classes varied in both methods, the overall accuracy and kappa values of LULC maps for pixel and object-based classification methods were 58.39%-0.45 and 89.58%-0.86, respectively. Accuracy assessments and comparative results showed that object-based classification gives better results for thematic LULC classes as well as the overall accuracy of LULC maps. Even though pixel-based classification method was good at mapping many thematic classes, there were misclassifications between natural/semi-natural LULC classes. These results can be attributed to parameters set by users, such as the number of control points, etc. However, the capacity of object-based classification method to include auxiliary data (e.g. DEM, NDVI) increases the accuracy of LULC maps with high-resolution satellites.