Lossless Compression of Hyperspectral Images Based on Searching Optimal Multibands for Prediction

This letter presents a lossless compression algorithm for hyperspectral images, which is based on the strength of correlations between bands. First, a searching model is constructed using the tree structure. Second, multibands which have strong correlations to each chosen band are found out and are...

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Bibliographic Details
Published in:IEEE geoscience and remote sensing letters 2009-04, Vol.6 (2), p.339-343
Main Authors: Chengfu Huo, Chengfu Huo, Rong Zhang, Rong Zhang, Tianxiang Peng, Tianxiang Peng
Format: Article
Language:English
Subjects:
Algorithms
Compressing
Compression algorithms
Correlation
Couple-group manner
Entropy
hyperspectral images
Hyperspectral imaging
Hyperspectral sensors
Image analysis
Image coding
Lossless
Mathematical models
Prediction algorithms
Searching
searching model
Table lookup
Tree data structures
tree structure
Vector quantization
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Summary:This letter presents a lossless compression algorithm for hyperspectral images, which is based on the strength of correlations between bands. First, a searching model is constructed using the tree structure. Second, multibands which have strong correlations to each chosen band are found out and are then used to predict the chosen band in a couple-group manner. Lastly, residual images are encoded using entropy coders. Experimental results show that our compression algorithm provides a competitive compression performance compared with most existing compression algorithms.
ISSN:1545-598X
1558-0571
DOI:10.1109/LGRS.2008.2012135