Multispectral Satellite Image Classification Based on Bare Bone Fireworks Algorithm

Eva Tuba, Raka Jovanovic, Milan Tuba

Research output: Chapter in Book/Report/Conference proceedingChapter


Satellite image classification is an important part of applications in various fields such as agriculture, environmental monitoring, and disaster management. K-means algorithm is a simple clustering method that can be adjusted for classification. Due to the fact that k-means represents a local search around the initially generated solutions, it should be combined with some global search method. We propose recent bare bone fireworks algorithm for k-means optimization used for image classification. The proposed method was tested on standard benchmark datasets and compared the results with other methods from the literature. Simulation results showed that the proposed combined approach is better for image classification compared to the original k-means algorithm, three other classification algorithms, and three methods based on other nature-inspired algorithms.

Original languageEnglish
Title of host publicationAdvances in Intelligent Systems and Computing
PublisherSpringer Verlag
Number of pages9
Publication statusPublished - 1 Jan 2020

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357



  • Bare bone fireworks algorithm
  • Classification
  • Metaheuristics
  • Multispectral satellite images
  • Optimization
  • Swarm intelligence

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science(all)

Cite this

Tuba, E., Jovanovic, R., & Tuba, M. (2020). Multispectral Satellite Image Classification Based on Bare Bone Fireworks Algorithm. In Advances in Intelligent Systems and Computing (pp. 305-313). (Advances in Intelligent Systems and Computing; Vol. 933). Springer Verlag.