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Self-Adaptive Hybrid PSO-GA Method for Change Detection Under Varying Contrast Conditions in Satellite Images
Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
KTO Karatay Univ, TUR.
2016 (English)In: Proceedings of the 2016 SAI Computing Conference (SAI), IEEE, 2016, 361-368 p.Conference paper (Refereed)
Abstract [en]

This paper proposes a new unsupervised satellite change detection method, which is robust to illumination changes. To achieve this, firstly, a preprocessing strategy is used to remove illumination artifacts and results in less false detection than traditional threshold-based algorithms. Then, we use the corrected input data to define a new fitness function based on the difference image. The purpose of using Self-Adaptive Hybrid Particle Swarm Optimization-Genetic Algorithm (SAPSOGA) is to combine two meta-heuristic optimization algorithms to search and find the feasible solution in the NP-hard change detection problem rapidly and efficiently. The hybrid algorithm is employed by letting the GA and PSO run simultaneously and similarities of GA and PSO have been considered to implement the algorithm, i.e. the population. In the SAPSOGA employed, in each iteration/generation the two population based algorithms share different amount of information or individual(s) between themselves. Thus, each algorithm informs each other about their best optimum results (fitness values and solution representations) which are obtained in their own population. The fitness function is minimized by using binary based SAPSOGA approach to produce binary change detection masks in each iteration to obtain the optimal change detection mask between two multi temporal multi spectral landsat images. The proposed approach effectively optimizes the change detection problem and finds the final change detection mask.

Place, publisher, year, edition, pages
IEEE, 2016. 361-368 p.
Keyword [en]
Remote sensing; Image processing; Optimization; Self-adaptive hybrid algorithm; Genetic algorithm; Binary particle swarm optimization
National Category
Computer and Information Science
Identifiers
URN: urn:nbn:se:bth-13683ISI: 000389451900050ISBN: 978-1-4673-8460-5 (print)OAI: oai:DiVA.org:bth-13683DiVA: diva2:1060802
Conference
SAI Computing Conference (SAI), JUL 13-15, 2016, London, ENGLAND
Available from: 2016-12-30 Created: 2016-12-30 Last updated: 2017-03-02Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf