Comparative Assessment of Pixel-Based and Object-Oriented Classification Techniques using Sentinel-2 Imagery of the Federal University of Technology, Akure (FUTA) Campus

Abstract

Precise classification of land cover is essential for effective environmental and urban planning, particularly in diverse landscapes with intricate spatial patterns. This study offers a comparative evaluation of pixel-based and object-oriented image classification techniques using Sentinel-2 satellite imagery of the Federal University of Technology, Akure (FUTA), Nigeria. The pixel-based classification applied the Maximum Likelihood Classification (MLC) method, which depended exclusively on spectral data, while the object-oriented approach integrated multi-resolution segmentation and contextual features such as shape and texture. Ground truth data were gathered from thirty (30) georeferenced locations using a mobile GPS for validation. Results show that while using the pixel-based method, the vegetation covers 2.515 km² (37%), compared to 2.266 km² (33%) from object-oriented classification; Farmland accounts for 1.917 km² (28%) versus 1.803 km² (27%); Bare Ground is recorded at 1.206 km² (18%) as opposed to 1.232 km² (18%); and Built-up is measured at 1.161 km² (17%) compared to 1.496 km² (22%) from the pixel-based classification. Accuracy assessments using confusion matrices revealed that the object-oriented method outperformed the pixel-based method, achieving an overall accuracy of 90% with a Kappa coefficient of 0.8663, compared to 80% accuracy for the pixel-based method. The object-oriented classification proved more effective in distinguishing built-up and bare ground areas, while both methods performed similarly in classifying vegetation. This study concludes that object-oriented classification is preferable for complex and urban environments where accuracy is critical. Expanding ground-truth data beyond thirty points and employing higher-resolution imagery would further enhance classification reliability and precision.