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A novel unsupervised Levy flight particle swarm optimization (ULPSO) method for multispectral remote-sensing image classification

Research output: Contribution to journalJournal article

Published
  • Huapeng Li
  • Shuqing Zhang
  • Ce Zhang
  • Ping Li
  • Roger Cropp
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<mark>Journal publication date</mark>2/12/2017
<mark>Journal</mark>International Journal of Remote Sensing
Issue number23
Volume38
Number of pages23
Pages (from-to)6970-6992
<mark>State</mark>Published
Early online date24/08/17
<mark>Original language</mark>English

Abstract

The rapid development of earth observation technology has produced large quantities of remote-sensing data. Unsupervised classification (i.e. clustering) of remote-sensing images, an important means to acquire land-use/cover information, has become increasingly in demand due to its simplicity and ease of application. Traditional methods, such as k-means, struggle to solve this NP-hard (Non-deterministic Polynomial hard) image classification problem. Particle swarm optimization (PSO), always achieving better result than k-means, has recently been applied to unsupervised image classification. However, PSO was also found to be easily trapped on local optima. This article proposes a novel unsupervised Levy flight particle swarm optimization (ULPSO) method for image classification with balanced exploitation and exploration capabilities. It benefits from a new searching strategy: the worst particle in the swarm is targeted and its position is updated with Levy flight at each iteration. The effectiveness of the proposed method was tested with three types of remote-sensing imagery (Landsat Thematic Mapper (TM), Flightline C1 (FLC), and QuickBird) that are distinct in terms of spatial and spectral resolution and landscape. Our results showed that ULPSO is able to achieve significantly better and more stable classification results than k-means and the other two intelligent methods based on genetic algorithm (GA) and particle swarm optimization (PSO) over all of the experiments. ULPSO is, therefore, recommended as an effective alternative for unsupervised remote-sensing image classification.

Bibliographic note

This is an Accepted Manuscript of an article published by Taylor //////