Staff Members - Benha University
Combining Pixel-Based and Object-Oriented Support Vector Machines using Bayesian Probability Theory
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences • 2014
Publication Information
Authors
Mahmoud Salah
Keywords
Pixel-based, object-oriented, SVMs, classification, Bayesian Probability Theory
Journal
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Publisher
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Volume
II-7
Issue
2014
Pages
67-74
publication.type
International
Paper Link
Not Available
Supplementary Materials
Not Available
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.
(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.