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Sparse coding of hyperspectral imagery using online learning

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Abstract

Sparse coding ensures to express the data in terms of a few nonzero dictionary elements. Since the data size is large for hyperspectral imagery, it is reasonable to use sparse coding for compression of hyperspectral images. In this paper, a hyperspectral image compression method is proposed using a discriminative online learning-based sparse coding algorithm. Compression and anomaly detection tests are performed on hyperspectral images from the AVIRIS dataset. Comparative rate–distortion analyses indicate that the proposed method is superior to the state-of-the-art hyperspectral compression techniques.

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Acknowledgments

This work is supported in part by the Scientific and Technical Research Council of Turkey under National Young Researchers Career Development Program (3501 TUBITAK CAREER) grant with agreement number 114E200. Authors are grateful to Mustafa Teke for his assistance in obtaining RX detection results. An earlier version of this study was presented in part at the IEEE International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM) 2014 [17].

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Çankaya University, 06790, Etimesgut, Ankara, Turkey

    İrem Ülkü & Behçet Uğur Töreyin

  2. Space Technologies Institute (UZAY), The Scientific and Technological Research Council of Turkey (TUBITAK), ODTÜ Yerleşkesi, 06800, Ankara, Turkey

    Behçet Uğur Töreyin

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  1. İrem Ülkü
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  2. Behçet Uğur Töreyin
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Correspondence to İrem Ülkü.

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Ülkü, İ., Töreyin, B.U. Sparse coding of hyperspectral imagery using online learning. SIViP 9, 959–966 (2015). https://doi.org/10.1007/s11760-015-0753-9

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  • DOI: https://doi.org/10.1007/s11760-015-0753-9

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