Radar Image Segmentation using Particle Swarm and Gravitational Search

Journal

International Journal of Digital Applications and Contemporary Research (IJDACR)

ISSN

2319-4863

Volume / Issue

Vol.4 · Issue 2

Published

September 2015

Access

Open Access

Licence

CC BY-NC-SA 4.0

Rubbaldeep Kaur Er. Pooja

Image segmentation can recognizes the areas of interest in a scene. Due to the presence of speckle noise, segmentation of Synthetic Aperture Radar (SAR) images is still a challenging problem. In this research paper we presented a radar image segmentation using modified particle swarm and gravitational search algorithm (PSO-GSA). In this method, threshold assessment is observed as an exploration process that examines for a suitable value in a continuous grayscale interval. Hence, proposed modified PSO-GSA algorithm is familiar to explore the optimal threshold. In order to provide an efficient fitness function with our proposed modified PSO-GSA algorithm, we assimilate the concept of grey number in Grey theory, maximum provisional entropy to get an enhanced two-dimensional grey entropy. In core, the segmentation speed of our proposed method owes to PSO-GSA algorithm, which has an owing convergence performance. Moreover, the segmentation quality of our proposed method is benefitted from the enhanced two-dimensional grey entropy, which results in mitigation of noise. Experimental results indicate that our method is superior to conventional PSO-GSA, GA based, AFS based and ABC based methods in terms of segmentation time and thresholding.

Rubbaldeep Kaur, Er. Pooja (2015). Radar Image Segmentation using Particle Swarm and Gravitational Search. International Journal of Digital Applications and Contemporary Research (IJDACR), Vol.4, Issue 2. ISSN: 2319-4863.

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