Remote sensing imagery segmentation based on multi-objective optimization algorithms

Salvador Hinojosa, Omar Avalos, Jorge Galvez, Diego Oliva, Erik Cuevas, Marco Perez-Cisneros

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Remote Sensing (RS) has been used to obtain relevant information about objects without the explicit necessity to stay in contact with them. RS collects measured data from the emanated energy of the surface of the earth. This process aims the construction of knowledge-based systems to identify interesting geographic features automatically. In RS, multispectral image segmentation is one of the most widespread methodologies for information extraction, using schemes comprising a wide variety of hard and soft grouping mechanisms based on different non-standard similarity measures making the classification problem to be application dependent. This procedure uses the spectral information contained in an image to recognize regions of interest. The segmentation of multispectral images is usually conducted by performing segmentation over a specific band according to the application. However, the segmentation of a specific channel might not perform well on the other bands of the image. This paper proposes a general scheme for multispectral imagery segmentation using multi-objective evolutionary algorithms (MOEAs) to identify thresholds encoding the best trade-offs between the segmentation criteria of various channels of the multispectral image. An evaluation of the performance of the proposed methodology is presented over a multispectral benchmark set composed of different images complexities and compared with several multi-objective algorithms.

Original languageEnglish
Title of host publication2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538646250
DOIs
Publication statusPublished - 23 Jan 2019
Externally publishedYes
Event2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018 - Gudalajara, Mexico
Duration: 6 Nov 20189 Nov 2018

Publication series

Name2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018

Conference

Conference2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018
CountryMexico
CityGudalajara
Period6.11.189.11.18

Keywords

  • evolutionary algorithms
  • image processing
  • multi-objective optimization
  • remote sensing
  • thresholding

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems and Management

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  • Cite this

    Hinojosa, S., Avalos, O., Galvez, J., Oliva, D., Cuevas, E., & Perez-Cisneros, M. (2019). Remote sensing imagery segmentation based on multi-objective optimization algorithms. In 2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018 [8625215] (2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LA-CCI.2018.8625215