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.
| Original language | English |
|---|---|
| Pages (from-to) | 959-966 |
| Number of pages | 8 |
| Journal | Signal, Image and Video Processing |
| Volume | 9 |
| Issue number | 4 |
| DOIs | |
| Publication status | Published - May 2015 |
| Externally published | Yes |
Bibliographical note
Publisher Copyright:© 2015, Springer-Verlag London.
Funding
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 [].
| Funders | Funder number |
|---|---|
| Türkiye Bilimsel ve Teknolojik Araştirma Kurumu | 114E200 |
Keywords
- Anomaly detection
- Hyperspectral imagery
- Online learning
- Sparse coding