Sparse coding of hyperspectral imagery using online learning

İrem Ülkü*, Behçet Uğur Töreyin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)959-966
Number of pages8
JournalSignal, Image and Video Processing
Issue number4
Publication statusPublished - May 2015
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2015, Springer-Verlag London.


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 [].

FundersFunder number
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu114E200


    • Anomaly detection
    • Hyperspectral imagery
    • Online learning
    • Sparse coding


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