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Modified residual method for the estimation of noise in hyperspectral images / Asad Mahmood in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1451 - 1460 Titre : Modified residual method for the estimation of noise in hyperspectral images Type de document : Article/Communication Auteurs : Asad Mahmood, Auteur ; Amandine Robin, Auteur ; Michael Sears, Auteur Année de publication : 2017 Article en page(s) : pp 1451 - 1460 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] bruit blanc [Termes IGN] corrélation [Termes IGN] corrélation automatique de points homologues [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] matrice de covariance [Termes IGN] résidu Résumé : (Auteur) Many hyperspectral image processing algorithms (e.g., detection, classification, endmember extraction, and so on) are generally designed with the assumption of no spectral or spatial correlation in noise. However, previous studies have shown the presence of nonnegligible correlation between the noise samples in different spectral bands, especially between noises in adjacent bands, and that most of the well-known intrinsic dimension estimation algorithms give poor estimates in the presence of correlated noise. Thus, there is a need to tackle the specific case of spectrally correlated noise for noise estimation. We show, in this paper, that the commonly employed hyperspectral noise estimation algorithm based on regression residuals can be significantly affected by spectrally correlated noise and we suggest a modified approach that proves to be robust to noise correlation. Furthermore, the proposed method improves the noise variance estimates in comparison to the classic residual method even for the case of uncorrelated noise. Simulation results show that the estimation error is reduced at times by a factor of 5 when there is high spectral correlation in the noise. Our proposed per-pixel noise estimator requires an estimate of the noise covariance matrix, and for this, we also propose a method to estimate the noise covariance matrix. Simulation results demonstrate that the per-pixel noise estimates obtained via the use of estimated noise statistics are almost as good as those obtained via use of the true statistics. Numéro de notice : A2017-155 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2624505 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2624505 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84690 [article]