Sparse coding of hyperspectral imagery using online learning

İrem Ülkü; Behçet Uğur Töreyin

doi:10.1007/s11760-015-0753-9

Sparse coding of hyperspectral imagery using online learning

İrem Ülkü^*, Behçet Uğur Töreyin

^*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: Dergiye katkı › Makale › bilirkişi

10 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Sayfa (başlangıç-bitiş)	959-966
Sayfa sayısı	8
Dergi	Signal, Image and Video Processing
Hacim	9
Basın numarası	4
DOI'lar	https://doi.org/10.1007/s11760-015-0753-9
Yayın durumu	Yayınlandı - May 2015
Harici olarak yayınlandı	Evet

Bibliyografik not

Publisher Copyright:
© 2015, Springer-Verlag London.

Finansman

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 [].

Finansörler	Finansör numarası
Türkiye Bilimsel ve Teknolojik Araştirma Kurumu	114E200

Belgeye Erişim

10.1007/s11760-015-0753-9

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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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.",

keywords = "Anomaly detection, Hyperspectral imagery, Online learning, Sparse coding",

author = "İrem {\"U}lk{\"u} and T{\"o}reyin, {Beh{\c c}et Uğur}",

note = "Publisher Copyright: {\textcopyright} 2015, Springer-Verlag London.",

year = "2015",

month = may,

doi = "10.1007/s11760-015-0753-9",

language = "English",

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AU - Töreyin, Behçet Uğur

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AB - 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.

KW - Anomaly detection

KW - Hyperspectral imagery

KW - Online learning

KW - Sparse coding

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JF - Signal, Image and Video Processing

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ER -

Sparse coding of hyperspectral imagery using online learning

Özet

Bibliyografik not

Finansman

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap