Combining Pixel-Based and Object-Oriented Support Vector Machines using Bayesian Probability Theory

Abstract. This study employed a hybrid system for the combination of pixel-based (PB) and object-oriented (OO) Support Vector Machines (SVMs) based on Bayesian Probability Theory (BPT) for improved land cover classification. A set of uncorrelated feature attributes have been generated from a one-meter IKONOS satellite image. Four different SVMs kernels were compared and tested to classify buildings, trees, roads and ground from satellite image and the generated attributes. The kernels used include: linear, polynomial, radial basis function (RBF), and sigmoid. PB and OO SVMs have been applied to classify the image. BPT was then applied for combining the class memberships from the PB and OO classifiers. Accuracy assessment was carried out using reference data sets derived from the one-meter IKONOS image. The outcomes demonstrate that the OO method has achieved an overall kappa coefficient of 0.8286, compared with 0.6327 that was derived from the conventional PB method. The improvement in overall kappa obtained from the combined system was 0.0608 over the OO SVMs.