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An entropy-based approach to automatic image segmentation of satellite images

Author

Listed:
  • Barbieri, Andre L.
  • de Arruda, G.F.
  • Rodrigues, Francisco A.
  • Bruno, Odemir M.
  • Costa, Luciano da Fontoura

Abstract

An entropy-based image segmentation approach is introduced and applied to color images obtained from Google Earth. Segmentation refers to the process of partitioning a digital image in order to locate different objects and regions of interest. The application to satellite images paves the way to automated monitoring of ecological catastrophes, urban growth, agricultural activity, maritime pollution, climate changing and general surveillance. Regions representing aquatic, rural and urban areas are identified and the accuracy of the proposed segmentation methodology is evaluated. The comparison with gray level images revealed that the color information is fundamental to obtain an accurate segmentation.

Suggested Citation

  • Barbieri, Andre L. & de Arruda, G.F. & Rodrigues, Francisco A. & Bruno, Odemir M. & Costa, Luciano da Fontoura, 2011. "An entropy-based approach to automatic image segmentation of satellite images," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(3), pages 512-518.
    • Handle: RePEc:eee:phsmap:v:390:y:2011:i:3:p:512-518
    DOI: 10.1016/j.physa.2010.10.015
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    References listed on IDEAS

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    1. Li, Baibing & Martin, Elaine B. & Morris, A. Julian, 2002. "On principal component analysis in L1," Computational Statistics & Data Analysis, Elsevier, vol. 40(3), pages 471-474, September.
    2. Illah Nourbakhsh & Randy Sargent & Anne Wright & Kathryn Cramer & Brian McClendon & Michael Jones, 2006. "Mapping disaster zones," Nature, Nature, vol. 439(7078), pages 787-788, February.
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    Cited by:

    1. Ma, Tinghuai & Li, Lu & Ji, Sai & Wang, Xin & Tian, Yuan & Al-Dhelaan, Abdullah & Al-Rodhaan, Mznah, 2014. "Optimized Laplacian image sharpening algorithm based on graphic processing unit," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 416(C), pages 400-410.
    2. Quispe, Laura V.C. & Tohalino, Jorge A.V. & Amancio, Diego R., 2021. "Using virtual edges to improve the discriminability of co-occurrence text networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 562(C).
    3. Lahmiri, Salim, 2016. "Image characterization by fractal descriptors in variational mode decomposition domain: Application to brain magnetic resonance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 235-243.
    4. Fabbri, Ricardo & Gonçalves, Wesley N. & Lopes, Francisco J.P. & Bruno, Odemir M., 2012. "Multi-q pattern analysis: A case study in image classification," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(19), pages 4487-4496.

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