Evolving Cellular Automata to Segment Hyperspectral Images Using Low Dimensional Images for Training

Part of the Lecture Notes in Computer Science book series (LNCS, volume 9108)

Abstract

This paper describes a hyperspectral image segmentation approach that has been developed to address the issues of lack of adequately labeled images, the computational load induced when using hyperspectral images in training and, especially, the adaptation of the level of segmentation to the desires of the users. The algorithm used is based on evolving cellular automata where the fitness is established based on the use of synthetic RGB images that are constructed on-line according to a set of parameters that define the type of segmentation the user wants. A series of segmentation experiments over real hyperspectral images are presented to show this adaptability and how the performance of the algorithm improves over other state of the art approaches found in the literature on the subject.

Keywords

Hyperspectral image segmentation Cellular automata Evolution 

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References

  1. 1.
    Darwish, A., Leukert, K., Reinhardt, W.: Image segmentation for the purpose of object-based classification. In: International Geoscience and Remote Sensing Symposium, vol. 3, pp. 2039–2041 (2003)Google Scholar
  2. 2.
    Tilton, J.C.: Analysis of hierarchically related image segmentations. In: IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, pp. 60–69 (2003)Google Scholar
  3. 3.
    Pesaresi, M., Benediktsson, J.A.: A new approach for the morphological segmentation of high-resolution satellite imagery. IEEE Transactions on Geoscience and Remote Sensing 39(2), 309–320 (2001)CrossRefGoogle Scholar
  4. 4.
    Farag, A.A., Mohamed, R.M., El-Baz, A.: A unified framework for MAP estimation in remote sensing image segmentation. IEEE Transactions on Geoscience and Remote Sensing 43(7), 1617–1634 (2005)CrossRefGoogle Scholar
  5. 5.
    Eches, O., Dobigeon, N., Tourneret, J.Y.: Markov random fields for joint unmixing and segmentation of hyperspectral images. In: 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), pp. 1–4 (2010)Google Scholar
  6. 6.
    Flouzat, G., Amram, O., Cherchali, S.: Spatial and spectral segmentation of satellite remote sensing imagery using processing graphs by mathematical morphology. In: IEEE International Geoscience and Remote Sensing Symposium Proceedings, IGARSS 1998, vol. 4, pp. 1–3 (1998)Google Scholar
  7. 7.
    Li, P.L.P., Xiao, X.X.X.: Evaluation of multiscale morphological segmentation of multispectral imagery for land cover classification. In: Proceedings of the 2004 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2004, 4, 0-3 (2004)Google Scholar
  8. 8.
    Quesada-Barriuso, P., Argello, F., Heras, D.B.: Efficient segmentation of hyperspectral images on commodity. Advances in Knowledge Based and Intelligent Information and Engineering Systems 243, 2130–2139 (2012)Google Scholar
  9. 9.
    Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognition 43(7), 2367–2379 (2010)CrossRefMATHGoogle Scholar
  10. 10.
    Li, J., Bioucas-Dias, J.M., Plaza, A.: Hyperspectral Image Segmentation Using a New Bayesian Approach with Active Learning. IEEE Transactions on Geoscience and Remote Sensing 49(10), 3947–3960 (2011)CrossRefGoogle Scholar
  11. 11.
    Veracini, T., Matteoli, S., Diani, M., Corsini, G.: Robust Hyperspectral Image Segmentation Based on a Non-Gaussian Model. In: 2010 2nd International Workshop on Cognitive Information Processing (CIP), pp. 192–197 (2010)Google Scholar
  12. 12.
    Duro, R.J., Lopez-Pena, F., Crespo, J.L.: Using Gaussian Synapse ANNs for Hyperspectral Image Segmentation and Endmember Extraction. In: Graña, M., Duro, R.J. (eds.) Computational Intelligence for Remote Sensing. SCI, vol. 133, pp. 341–362. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Priego, B., Souto, D., Bellas, F., Duro, R.J.: Hyperspectral image segmentation through evolved cellular automata. Pattern Recognition Letters 34(14), 1648–1658 (2013)CrossRefGoogle Scholar
  14. 14.
    Storn, R., Price, K.: Differential evolution a simple and efficient heuristic for global optimization over continuous spaces. Journal of Global Optimization 11(4), 341–359 (1997)CrossRefMATHMathSciNetGoogle Scholar
  15. 15.
    Benediktsson, J., Pesaresi, M., Amason, K.: Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Trans. Geosci. Remote Sens. 41(9), 1940–1949 (2003)CrossRefGoogle Scholar
  16. 16.
    Marpu, P.R., Pedergnana, M., Mura, M.D., Benediktsson, J.A., Bruzzone, L.: Automatic generation of standard deviation attribute profiles for spectral-spatial classification of remote sensing data. IEEE Geosci. Remote Sens. Lett. 10(2), 293–297 (2013)CrossRefGoogle Scholar
  17. 17.
    Lopez-Fandino, J., Quesada-Barriuso, P., Heras, D., Arguello, F.: Efficient ELM-Based Techniques for the Classification of Hyperspectral Remote Sensing Images on Commodity GPUs. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing PP(99), 1–10 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Integrated Group for Engineering ResearchUniversidade da CoruñaA CoruñaSpain

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