Research Article Open Access

An Automatic and Accurate Segmentation for High Resolution Satellite Image

Abstract

Satellite images need to be classified for surveillance purposes. The image obtained from the satellite should be classified properly in order to obtain the fine details of the image. Two method of image classification techniques are available they are supervised image classification and unsupervised image classification. Partitioning satellite image into meaningful regions has played an important role in recent years. The combination of topic models and random fields has been successfully applied to image classification because of their complementary effect. The supervised image classification needs manual interpretation with plentiful and expensive human effort. We proposed an efficient unsupervised image classification method for the classification of satellite images. The methods used are Latent Dirichlet Allocation and Markov Random field .The classification or segmentation obtained from the Latent Dirichlet Allocation method is more reliant on content coherence. The annotation performance of large satellite images is the benefit obtained from the topic models. The Markov Random Field method is used to obtain the spatial information between the neighbouring regions in the image. The combined models are complementary and the segmentation accuracy is improved. The iterative algorithm is proposed to make the number of classes finally be converged to appropriate levels. This is based on label cost and Bayesian information criterion .The whole process works automatically instead of assuming it beforehand as a constant.

S.Saumya, D.V.Jiji Thanka Ligoshia

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