Predictive partitioned vector quantization for hyperspectral sounder data compression

Bormin Huang; Alok Ahuja; Hung-Lung Allen Huang; Timothy J. Schmit; Roger W. Heymann

doi:10.1117/12.560402

14 October 2004 Predictive partitioned vector quantization for hyperspectral sounder data compression

Bormin Huang, Alok Ahuja, Hung-Lung Allen Huang, Timothy J. Schmit, Roger W. Heymann

Author Affiliations +

Proceedings Volume 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective; (2004) https://doi.org/10.1117/12.560402
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

The compression of three-dimensional hyperspectral sounder data is a challenging task given its unprecedented size and nature. Vector quantization (VQ) is explored for the compression of this hyperspectral sounder data. The high dimensional vectors are partitioned into subvectors to reduce codebook search and storage complexity in coding of the data. The partitions are made by use of statistical properties of the sounder data in the spectral dimension. Moreover, the data is decorrelated at first to make it better suited for vector quantization. Due to the data characteristics, the iterative codebook generation procedure converges much faster and also leads to a better reconstruction of the sounder data. For lossless compression of the hyperspectral sounder data, the residual error and the quantization indices are entropy coded. The independent vector quantizers for different partitions make this scheme practical for compression of the large volume 3D hyperspectral sounder data.

Citation Download Citation

Bormin Huang, Alok Ahuja, Hung-Lung Allen Huang, Timothy J. Schmit, and Roger W. Heymann "Predictive partitioned vector quantization for hyperspectral sounder data compression", Proc. SPIE 5548, Atmospheric and Environmental Remote Sensing Data Processing and Utilization: an End-to-End System Perspective, (14 October 2004); https://doi.org/10.1117/12.560402

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE