Watershed image analysis using a PSO-CA hybrid approach

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKapitel samlingsverk

Abstract

Pixel classification of watershed satellite image is a challenging task in remote sensing. Uses of Particle Swarm Optimisation and Cellular Automata are significant methods in watershed image segmentation. This paper proposes a method of pixel classification using a new hybrid Particle Swarm Optimization-Cellular Automata approach. The proposed unsupervised method identifies clusters using 2-Dimensional Cellular Automata model over particle swarm optimization. PSO is an optimization stochastic method based on populations, following the social behavior like bird flocks. This new method identifies vague clusters utilizing initial fuzzy membership values. Cellular Automata is a dynamic and discrete model comprises of inter-connected cells uniforming with states. We utilize the 2D Cellular Automata method on the Barakar River catchment area. The segmented regions are compared with existing methods which shows superiority of our new method.

Detaljer

Författare
Externa organisationer
  • Government College of Engineering & Leather Technology, Kolkata
  • Jadavpur University
Originalspråkengelska
Titel på värdpublikationHybrid Intelligent Techniques for Pattern Analysis and Understanding
FörlagCRC Press
Sidor229-246
Antal sidor18
ISBN (elektroniskt)9781498769372
ISBN (tryckt)9781498769358
StatusPublished - 2017 jan 1
PublikationskategoriForskning
Peer review utfördJa
Externt publiceradJa