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A Single Model CNN for Hyperspectral Image Denoising / Alessandro Maffei in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2516 - 2529 Titre : A Single Model CNN for Hyperspectral Image Denoising Type de document : Article/Communication Auteurs : Alessandro Maffei, Auteur ; Juan Mario Haut, Auteur ; Mercedes Eugenia Paoletti, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2516 - 2529 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] bande spectrale [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] filtrage d'information [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes IGN] information géographique [Termes IGN] signature spectrale Résumé : (auteur) Denoising is a common preprocessing step prior to the analysis and interpretation of hyperspectral images (HSIs). However, the vast majority of methods typically adopted for HSI denoising exploit architectures originally developed for grayscale or RGB images, exhibiting limitations when processing high-dimensional HSI data cubes. In particular, traditional methods do not take into account the high spectral correlation between adjacent bands in HSIs, which leads to unsatisfactory denoising performance as the rich spectral information present in HSIs is not fully exploited. To overcome this limitation, this article considers deep learning models—such as convolutional neural networks (CNNs)—to perform spectral–spatial HSI denoising. The proposed model, called HSI single denoising CNN (HSI-SDeCNN), efficiently takes into consideration both the spatial and spectral information contained in HSIs. Experimental results on both synthetic and real data demonstrate that the proposed HSI-SDeCNN outperforms other state-of-the-art HSI denoising methods. Source code: https://github.com/mhaut/HSI-SDeCNN Numéro de notice : A2020-199 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2952062 Date de publication en ligne : 26/11/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2952062 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94869 [article]