SAR Image Segmentation using Vector Quantization Technique on Entropy Images
Abstract
The development and application of various remote sensing platforms result in the production of huge amounts of satellite image data. Therefore, there is an increasing need for effective querying and browsing in these image databases. In order to take advantage and make good use of satellite images data, we must be able to extract meaningful information from the imagery. Hence we proposed a new algorithm for SAR image segmentation. In this paper we propose segmentation using vector quantization technique on entropy image. Initially, we obtain entropy image and in second step we use Kekre's Fast Codebook Generation (KFCG) algorithm for segmentation of the entropy image. Thereafter, a codebook of size 128 was generated for the Entropy image. These code vectors were further clustered in 8 clusters using same KFCG algorithm and converted into 8 images. These 8 images were displayed as a result. This approach does not lead to over segmentation or under segmentation. We compared these results with well known Gray Level Co-occurrence Matrix. The proposed algorithm gives better segmentation with less complexity.
Keywords
Cite
@article{arxiv.1004.1789,
title = {SAR Image Segmentation using Vector Quantization Technique on Entropy Images},
author = {H. B. Kekre and Saylee Gharge and Tanuja K. Sarode},
journal= {arXiv preprint arXiv:1004.1789},
year = {2010}
}
Comments
IEEE Publication format, International Journal of Computer Science and Information Security, IJCSIS, Vol. 7 No. 3, March 2010, USA. ISSN 1947 5500, http://sites.google.com/site/ijcsis/