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Lattice Auto-Associative Memories Induced Multivariate Morphology for Hyperspectral Image Spectral-Spatial Classification

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Progress in Computer Recognition Systems (CORES 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 977))

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Abstract

The simultaneous use of spatial and spectral information for the classification-based analysis of hyperspectral images improves classification results and image segmentation quality. In this paper pixel spectra are individually classified by conventional support vector machines (SVM). The result of an innovative watershed transformation is used to postprocess the SVM result, imposing the homogeneity of the watershed region, i.e. classification disagreements inside the watershed region are solved by majority voting. This paper introduces several approaches to define reduced supervised orderings based on the recall distance of lattice auto-associative memories (LAAM). The automatic unsupervised selection of the foreground/background training sets from the hyperspectral image data is performed by the use of endmember induction algorithms (EIA). The proposed approach is compared with a recent state-of-the-art spectral-spatial approach.

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Acknowledgements

M.A. Veganzones carried some of the computational experiments reported in this paper. The work has been partially funded by the Basque Government grant IT874-13 for the GIC research group and by FEDER funds through MINECO project TIN2017-85827-P.

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Authors and Affiliations

  1. Grupo de Inteligencia Computacional, University of the Basque Country (UPV/EHU), San Sebastian, Spain

    Manuel Graña

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  1. Manuel Graña
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Correspondence to Manuel Graña .

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Editors and Affiliations

  1. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Robert Burduk

  2. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Marek Kurzynski

  3. Faculty of Electronics, Wroclaw University of Science and Technology, Wrocław, Poland

    Michał Wozniak

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Graña, M. (2020). Lattice Auto-Associative Memories Induced Multivariate Morphology for Hyperspectral Image Spectral-Spatial Classification. In: Burduk, R., Kurzynski, M., Wozniak, M. (eds) Progress in Computer Recognition Systems. CORES 2019. Advances in Intelligent Systems and Computing, vol 977. Springer, Cham. https://doi.org/10.1007/978-3-030-19738-4_32

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