Particle swarm optimization-based despeckling and decluttering of wavelet packet transformed synthetic aperture radar images

Perumal Vasuki; S. Mohamed Mansoor Roomi

doi:10.1117/1.JRS.7.073461

16 December 2013 Particle swarm optimization-based despeckling and decluttering of wavelet packet transformed synthetic aperture radar images

Perumal Vasuki, S. Mohamed Mansoor Roomi

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 7, Issue 1, 073461 (December 2013). https://doi.org/10.1117/1.JRS.7.073461

Abstract

The performance of automatic target recognition in synthetic aperture radar images is greatly influenced by preprocessing, viz, despeckling and decluttering. In this work, a particle swarm optimization (PSO)-based adaptive wavelet packet transform is introduced for despeckling and decluttering of military targets including tanks, bulldozers, trucks, cars, cannons, and armored personnel carriers. The proposed method consists of two stages. The first stage removes speckle, and the second stage removes clutter with the aid of PSO to optimize the objective criteria, such as equivalent number of looks and signal to clutter ratio, respectively. The purpose of these methods is to enhance the target feature suitable for further processing. The proposed work has been tested on the moving and stationary target acquisition and recognition database and shows a remarkable performance over existing methods.

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Perumal Vasuki and S. Mohamed Mansoor Roomi "Particle swarm optimization-based despeckling and decluttering of wavelet packet transformed synthetic aperture radar images," Journal of Applied Remote Sensing 7(1), 073461 (16 December 2013). https://doi.org/10.1117/1.JRS.7.073461

Published: 16 December 2013

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