Abstract
In this paper, a new algorithm for lossless compression of hyperspectral images is proposed. The spectral redundancy in hyperspectral images is exploited using a context-match method driven by the correlation between adjacent bands. This method is suitable for hyperspectral images in the band-sequential format. Moreover, this method compares favorably with the recent proposed lossless compression algorithms in terms of compression, with a lower complexity.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 4187-4193 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Geoscience and Remote Sensing |
| Volume | 45 |
| Issue number | 12 |
| DOIs | |
| State | Published - Dec 2007 |
Keywords
- Conditional average
- Context coding
- Correlation
- Entropy code
- Golomb-rice code
- Hyperspectral image
- Image coding
ASJC Scopus subject areas
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)
Fingerprint Dive into the research topics of 'Lossless hyperspectral-image compression using context-based conditional average'. Together they form a unique fingerprint.
Cite this
Wang, H., Babacan, S. D., & Sayood, K. (2007). Lossless hyperspectral-image compression using context-based conditional average. IEEE Transactions on Geoscience and Remote Sensing, 45(12), 4187-4193. https://doi.org/10.1109/TGRS.2007.906085