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Mathematical Problems in Engineering
Volume 2015 (2015), Article ID 182439, 8 pages
Research Article

Object-Oriented Semisupervised Classification of VHR Images by Combining MedLDA and a Bilateral Filter

1The Academy of Disaster Reduction and Emergency Management, Beijing Normal University, Beijing 100875, China
2The State-Province Joint Engineering Laboratory of Spatial Information Technology for High-Speed Railway Safety and Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China

Received 8 July 2015; Accepted 29 October 2015

Academic Editor: Fons J. Verbeek

Copyright © 2015 Shi He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


A Bayesian hierarchical model is presented to classify very high resolution (VHR) images in a semisupervised manner, in which both a maximum entropy discrimination latent Dirichlet allocation (MedLDA) and a bilateral filter are combined into a novel application framework. The primary contribution of this paper is to nullify the disadvantages of traditional probabilistic topic models on pixel-level supervised information and to achieve the effective classification of VHR remote sensing images. This framework consists of the following two iterative steps. In the training stage, the model utilizes the central labeled pixel and its neighborhood, as a squared labeled image object, to train the classifiers. In the classification stage, each central unlabeled pixel with its neighborhood, as an unlabeled object, is classified as a user-provided geoobject class label with the maximum posterior probability. Gibbs sampling is adopted for model inference. The experimental results demonstrate that the proposed method outperforms two classical SVM-based supervised classification methods and probabilistic-topic-models-based classification methods.