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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
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)
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)
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)
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)
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)
Tarabalka, Y., Chanussot, J., Benediktsson, J.A.: Segmentation and classification of hyperspectral images using watershed transformation. Pattern Recognition 43(7), 2367–2379 (2010)
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)
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)
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)
Priego, B., Souto, D., Bellas, F., Duro, R.J.: Hyperspectral image segmentation through evolved cellular automata. Pattern Recognition Letters 34(14), 1648–1658 (2013)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Priego, B., Bellas, F., Duro, R.J. (2015). Evolving Cellular Automata to Segment Hyperspectral Images Using Low Dimensional Images for Training. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo-Moreo, F., Adeli, H. (eds) Bioinspired Computation in Artificial Systems. IWINAC 2015. Lecture Notes in Computer Science(), vol 9108. Springer, Cham. https://doi.org/10.1007/978-3-319-18833-1_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-18833-1_13
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18832-4
Online ISBN: 978-3-319-18833-1
eBook Packages: Computer ScienceComputer Science (R0)