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Performance of Wavelets for Information Preservation in Hyperspectral Image Compression

Sonal S. Save, R. R. Sedamkar Published in Image Processing

International Journal of Applied Information Systems
Year of Publication: 2015
© 2015 by IJAIS Journal
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  1. Sonal S Save and R R Sedamkar. Article: Performance of Wavelets for Information Preservation in Hyperspectral Image Compression. International Journal of Applied Information Systems 9(2):11-16, June 2015. BibTeX

    	author = "Sonal S. Save and R. R. Sedamkar",
    	title = "Article: Performance of Wavelets for Information Preservation in Hyperspectral Image Compression",
    	journal = "International Journal of Applied Information Systems",
    	year = 2015,
    	volume = 9,
    	number = 2,
    	pages = "11-16",
    	month = "June",
    	note = "Published by Foundation of Computer Science, New York, USA"


This paper investigates various wavelets in terms of information preservation in hyperspectral image analysis. The compression method uses Principal Component Analysis (PCA) to provide spectral decorrelation and also dimensionality reduction. Principal Component (PC) images are then compressed by various wavelets and Set Partitioning in Hierarchical Trees (SPIHT) based coder. Experimental results by using five wavelets show that the compression method preserves spatial details and spectral features for all wavelets. Among the five wavelets used, coiflet achieves higher signal-to-noise ratio at high compression in spectral dimension. Performance is best when a few (10 or less than 10) PCs are retained for coding. The order of performance is coiflet2, biorthogonal2. 2, symlet2, daubechies4 and biorthogonal1. 1 for given AVIRIS dataset.


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Dimensionality reduction, Information preservation, Spectral angle mapper, covariance matrix