VST-based lossy compression of hyperspectral data for new generation sensors

Alexander N. Zemliachenko; Ruslan A. Kozhemiakin; Mykhail L. Uss; Sergey K. Abramov; Vladimir V. Lukin; Benoit Vozel; Kacem Chehdi

doi:10.1117/12.2028415

17 October 2013 VST-based lossy compression of hyperspectral data for new generation sensors

Alexander N. Zemliachenko, Ruslan A. Kozhemiakin, Mykhail L. Uss, Sergey K. Abramov, Vladimir V. Lukin, Benoit Vozel, Kacem Chehdi

Author Affiliations +

Proceedings Volume 8892, Image and Signal Processing for Remote Sensing XIX; 88920L (2013) https://doi.org/10.1117/12.2028415
Event: SPIE Remote Sensing, 2013, Dresden, Germany

Abstract

This paper addresses lossy compression of hyperspectral images acquired by sensors of new generation for which signaldependent component of the noise is prevailing compared to the noise-independent component. First, for sub-band (component-wise) compression, it is shown that there can exist an optimal operation point (OOP) for which MSE between compressed and noise-free image is minimal, i.e., maximal noise filtering effect is observed. This OOP can be observed for two approaches to lossy compression where the first one presumes direct application of a coder to original data and the second approach deals with applying direct and inverse variance stabilizing transform (VST). Second, it is demonstrated that the second approach is preferable since it usually provides slightly smaller MSE and slightly larger compression ratio (CR) in OOP. One more advantage of the second approach is that the coder parameter that controls CR can be set fixed for all sub-band images. Moreover, CR can be considerably (approximately twice) increased if sub-band images after VST are grouped and lossy compression is applied to a first sub-band image in a group and to “difference” images obtained for this group. The proposed approach is tested for Hyperion hyperspectral images and shown to provide CR about 15 for data compression in the neighborhood of OOP.

Citation Download Citation

Alexander N. Zemliachenko, Ruslan A. Kozhemiakin, Mykhail L. Uss, Sergey K. Abramov, Vladimir V. Lukin, Benoit Vozel, and Kacem Chehdi "VST-based lossy compression of hyperspectral data for new generation sensors", Proc. SPIE 8892, Image and Signal Processing for Remote Sensing XIX, 88920L (17 October 2013); https://doi.org/10.1117/12.2028415

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

Includes PDF, HTML & Video, when available