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A novel unmixing-based hypersharpening method via convolutional neural network / Xiaochen Lu in IEEE Transactions on geoscience and remote sensing, vol 60 n° 1 (January 2022)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 60 n° 1 (January 2022) . - n° 5503614 Titre : A novel unmixing-based hypersharpening method via convolutional neural network Type de document : Article/Communication Auteurs : Xiaochen Lu, Auteur ; Tong Li, Auteur ; Junping Zhang, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 5503614 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] fusion d'images [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] pansharpening (fusion d'images) [Termes IGN] pouvoir de résolution géométrique [Termes IGN] pouvoir de résolution spectrale Résumé : (auteur) Hypersharpening (namely, hyperspectral (HS) and multispectral (MS) image fusion) aims at enhancing the spatial resolution of HS image via an auxiliary higher resolution MS image. Currently, numerous hypersharpening methods are proposed successively, among which the unmixing-based approaches have been widely researched and demonstrated their effectiveness in the spectral fidelity aspect. However, existing unmixing-based fusion methods substantially employ mathematical techniques to solve the spectral mixture model, without taking full advantage of the collaborative spatial–spectral information that is usually helpful for abundance estimation improvement. To overcome this drawback, in this article, a novel unmixing-based HS and MS image fusion method, via a convolutional neural network (CNN), is proposed to promote spectral fidelity. The main idea of this work is to use CNN to fully explore the spatial information and the spectral information of both HS and MS images simultaneously, thereby enhancing the accuracy of estimating the abundance maps. Experiments on four simulated and real remote sensing data sets demonstrate that the proposed method is beneficial to the spectral fidelity of the fused images compared with some state-of-the-art algorithms. Meanwhile, it is also easy to implement and has a certain advantage in running time. Numéro de notice : A2022-028 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2021.3063105 Date de publication en ligne : 22/03/2021 En ligne : https://doi.org/10.1109/TGRS.2021.3063105 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99264 [article]

Modeling and simulation of polarimetric hyperspectral imaging process / Junping Zhang in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 6 (June 2012) . - pp 2238 - 2253 Titre : Modeling and simulation of polarimetric hyperspectral imaging process Type de document : Article/Communication Auteurs : Junping Zhang, Auteur ; J. Chen, Auteur ; B. Zou, Auteur ; Y. Zhang, Auteur Année de publication : 2012 Article en page(s) : pp 2238 - 2253 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] données polarimétriques [Termes IGN] image hyperspectrale [Termes IGN] modélisation de prise de vue [Termes IGN] polarisation [Termes IGN] réflectance végétale [Termes IGN] simulation d'image Résumé : (Auteur) Polarimetric hyperspectral images can provide spectral, spatial, and polarimetric information of a scene, which are unique and comprehensive for remote sensing applications such as growth monitoring of crops, analysis of water quality, and geology mapping, etc. The researches on polarimetric hyperspectral imaging mechanism and on image characteristics are of great importance for further information extraction and utilization of the images. The purposes of this paper are to analyze the mechanism of polarimetric hyperspectral imaging and to model such a process. The outcome of the paper will help designers and users of a polarimetric hyperspectral imaging system to further understand the system and take full advantages of it. In this paper, a polarimetric hyperspectral imaging model is proposed, in which the influence of skylight on polarization is considered, and subpixel model, polarized reflectance models, and the classical fast canopy reflectance model are combined to model the vegetation canopy. Then, a simulated scene that includes a woodland area with low shrubbery and a road is obtained by using the imaging model. Experiments analyze and discuss the simulation condition and parameters of the imaging models, the uniqueness, and usefulness of the integration of polarimetric and spectral information. Numéro de notice : A2012-265 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2172618 Date de publication en ligne : 28/11/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2172618 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31711 [article]

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