Image and Signal Processing Methods

Lossless compression of hyperspectral images using conventional recursive least-squares predictor with adaptive prediction bands

[+] Author Affiliations
Fang Gao, Shuxu Guo

Jilin University, College of Electronic Science and Engineering, No. 2699 Qianjin Road, Changchun 130021, Jilin, China

J. Appl. Remote Sens. 10(1), 015010 (Feb 10, 2016). doi:10.1117/1.JRS.10.015010
History: Received August 25, 2015; Accepted January 19, 2016
Text Size: A A A

Abstract.  An efficient lossless compression scheme for hyperspectral images using conventional recursive least-squares (CRLS) predictor with adaptive prediction bands is proposed. The proposed scheme first calculates the preliminary estimates to form the input vector of the CRLS predictor. Then the number of bands used in prediction is adaptively selected by an exhaustive search for the number that minimizes the prediction residual. Finally, after prediction, the prediction residuals are sent to an adaptive arithmetic coder. Experiments on the newer airborne visible/infrared imaging spectrometer (AVIRIS) images in the consultative committee for space data systems (CCSDS) test set show that the proposed scheme yields an average compression performance of 3.29(bits/pixel), 5.57(bits/pixel), and 2.44(bits/pixel) on the 16-bit calibrated images, the 16-bit uncalibrated images, and the 12-bit uncalibrated images, respectively. Experimental results demonstrate that the proposed scheme obtains compression results very close to clustered differential pulse code modulation-with-adaptive-prediction-length, which achieves best lossless compression performance for AVIRIS images in the CCSDS test set, and outperforms other current state-of-the-art schemes with relatively low computation complexity.

Figures in this Article
© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Fang Gao and Shuxu Guo
"Lossless compression of hyperspectral images using conventional recursive least-squares predictor with adaptive prediction bands", J. Appl. Remote Sens. 10(1), 015010 (Feb 10, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.015010


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

PubMed Articles
Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.