Extending the fractional order Darwinian particle swarm optimization to segmentation of hyperspectral images

Abstract

Hyperspectral sensors generate detailed information about the earth’s surface and climate in numerous contiguous narrow spectral bands, being widely used in resource management, agriculture, environmental monitoring, and others. However, due to the high dimensionality of hyperspectral data, it is difficult to design accurate and efficient image segmentation algorithms for hyperspectral imagery. In this paper a new multilevel thresholding method for segmentation of hyperspectral images into different homogenous regions is proposed. The new method is based on the Fractional-Order Darwinian Particle Swarm Optimization (FODPSO) which exploits the many swarms of test solutions that may exist at any time. In addition, the concept of fractional derivative is used to control the convergence rate of particles. The FODPSO is used to solve the so-called Otsu problem for each channel of the hyperspectral data as a grayscale image that indicates the spectral response to a particular frequency in the electromagnetic spectrum. In other words, the problem of n-level thresholding is reduced to an optimization problem in order to search for the thresholds that maximize the between-class variance. Experimental results successfully compare the FODPSO with the traditional PSO for multi-level segmentation of hyperspectral images. The FODPSO acts better than the other method in terms of both CPU time and fitness, thus being able to find the optimal set of thresholds with a larger between-class variance in less computational time.