A Comparison of Object-Based Classifiers for Land Cover Classification of Ankobra River Basin in Ghana

Remote sensing and GIS techniques on land cover classification using traditional pixel-based classification have been widely used. However, pixel-based classification uses only the spectral information during classification, which has some limitations. Object-based classification compensates for these limitations by merging the spectral and spatial information during the image segmentation phase. Using object-based approaches has allowed researchers to compare different machine-learning classifiers. Previous studies have shown that using different classifiers may lead to different classification accuracies. This has resulted in many studies investigating the effectiveness and efficiency of different classifiers. In this study, the performances of Naïve Bayes (NB), K-Nearest Neighbour (KNN), and Support Vector Machine (SVM) classifiers in object-based landcover classification using Landsat Imagery were explored. The Ankobra River Basin was used as the study area, with four land cover classes (Forest, Built-Up, Vegetation (grassland) and Water Bodies), based on 946 training datasets. A comparative assessment of the results showed that SVM and NB were superior to KNN. The SVM produced the highest overall accuracy (99.65%), followed by NB (95.65%) and lastly KNN (55.79%). A 95% confidence level statistical test was also carried out on the classifiers. SVM was identified as an effective and robust classification algorithm for performing object-based image analysis (OBIA) compared to NB and KNN. When implementing an Object-Based Image Analysis (OBIA) approach for land cover assessment, there is the need for appropriate tuning of parameters, and enough training samples since it affects the classification accuracy.