Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data

Naoufal Amrani, Joan Serra-Sagrista, Valero Laparra, Michael W. Marcellin, Jesus Malo

Research output: Contribution to journalArticleResearchpeer-review

31 Citations (Scopus)

Abstract

© 2016 IEEE. A novel wavelet-based scheme to increase coefficient independence in hyperspectral images is introduced for lossless coding. The proposed regression wavelet analysis (RWA) uses multivariate regression to exploit the relationships among wavelet-transformed components. It builds on our previous nonlinear schemes that estimate each coefficient from neighbor coefficients. Specifically, RWA performs a pyramidal estimation in the wavelet domain, thus reducing the statistical relations in the residuals and the energy of the representation compared to existing wavelet-based schemes. We propose three regression models to address the issues concerning estimation accuracy, component scalability, and computational complexity. Other suitable regression models could be devised for other goals. RWA is invertible, it allows a reversible integer implementation, and it does not expand the dynamic range. Experimental results over a wide range of sensors, such as AVIRIS, Hyperion, and Infrared Atmospheric Sounding Interferometer, suggest that RWA outperforms not only principal component analysis and wavelets but also the best and most recent coding standard in remote sensing, CCSDS-123.
Original languageEnglish
Article number7487041
Pages (from-to)5616-5627
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume54
Issue number9
DOIs
Publication statusPublished - 1 Sep 2016

Keywords

  • Redundancy in hyperspectral images
  • remote sensing data compression
  • transform coding via regression
  • wavelet-based transform coding

Fingerprint Dive into the research topics of 'Regression Wavelet Analysis for Lossless Coding of Remote-Sensing Data'. Together they form a unique fingerprint.

Cite this