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Spatial-spectral operator theoretic methods for hyperspectral image classification

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Abstract

With the emergence of new remote sensing modalities, it becomes increasingly important to find novel algorithms for fusion and integration of different types of data for the purpose of improving performance of applications, such as target/anomaly detection or classification. Many popular techniques that deal with this problem are based on performing multiple classifications and fusing these individual results into one product. In this paper we provide a new approach, focused on creating joint representations of the multi-modal data, which then can be subject to analysis by state of the art classifiers. In the work presented in this paper we consider the problem of spatial-spectral fusion for hyperspectral imagery. Our approach involves machine learning techniques based on analysis of joint data-dependent graphs and the resulting data-dependent fusion operators and their representations.

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Acknowledgments

The authors would like to thank Professor Landgrebe (Purdue University, USA) for providing the Indian Pines data and Professor Paolo Gamba (Pavia University, Italy) for providing the Pavia University and Pavia Centre data. The work presented in this paper was supported in part by NSF through grant CBET 0854223, by DTRA through Grant HDTRA 1-13-1-0015, and by ARO through Grant W911NF1610008.

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Authors and Affiliations

  1. Department of Mathematics, University of Maryland, College Park, MD, USA

    John J. Benedetto, Wojciech Czaja & Timothy Doster

  2. Department of Mathematics, Case Western Reserve University, Clevland, OH, USA

    Julia Dobrosotskaya

  3. Department of Mathematics and Statistics, American University, Washington, DC, USA

    Kevin Duke

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  1. John J. Benedetto
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  2. Wojciech Czaja
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  4. Timothy Doster
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  5. Kevin Duke
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Correspondence to Wojciech Czaja.

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Benedetto, J.J., Czaja, W., Dobrosotskaya, J. et al. Spatial-spectral operator theoretic methods for hyperspectral image classification. Int J Geomath 7, 275–297 (2016). https://doi.org/10.1007/s13137-016-0085-0

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