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

Shi He, Hong Tang, Jing Li, Yang Shu and Li Shen

Mathematical Problems in Engineering, 2015, vol. 2015, 1-8

Abstract:

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.

Date: 2015
References: Add references at CitEc
Citations:

Downloads: (external link)
http://downloads.hindawi.com/journals/MPE/2015/182439.pdf (application/pdf)
http://downloads.hindawi.com/journals/MPE/2015/182439.xml (text/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:182439

DOI: 10.1155/2015/182439

Access Statistics for this article

More articles in Mathematical Problems in Engineering from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().