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Regression wavelet analysis for lossless coding of remote-sensing data / Naoufal Amrani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 9 (September 2016) . - pp 5616 - 5627 Titre : Regression wavelet analysis for lossless coding of remote-sensing data Type de document : Article/Communication Auteurs : Naoufal Amrani, Auteur ; Joan Serra-Sagristà, Auteur ; Valero Laparra, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 5616 - 5627 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] décomposition d'image [Termes IGN] image hyperspectrale [Termes IGN] ondelette [Termes IGN] régression [Termes IGN] transformation en ondelettes Résumé : (Auteur) 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. Numéro de notice : A2016-905 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2569485 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2569485 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83100 [article]