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An integrated approach to registration and fusion of hyperspectral and multispectral images / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3020 - 3033 Titre : An integrated approach to registration and fusion of hyperspectral and multispectral images Type de document : Article/Communication Auteurs : Yuan Zhou, Auteur ; Anand Rangarajan, Auteur ; Paul D. Gader, Auteur Année de publication : 2020 Article en page(s) : pp 3020 - 3033 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme de fusion [Termes IGN] distorsion d'image [Termes IGN] fusion d'images [Termes IGN] image à haute résolution [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] méthode des moindres carrés [Termes IGN] points registration [Termes IGN] tâche image d'un point Résumé : (auteur) Combining a hyperspectral (HS) image and a multispectral (MS) image—an example of image fusion—can result in a spatially and spectrally high-resolution image. Despite the plethora of fusion algorithms in remote sensing, a necessary prerequisite, namely registration, is mostly ignored. This limits their application to well-registered images from the same source. In this article, we propose and validate an integrated registration and fusion approach (code available at https://github.com/zhouyuanzxcv/Hyperspectral ). The registration algorithm minimizes a least-squares (LSQ) objective function with the point spread function (PSF) incorporated together with a nonrigid freeform transformation applied to the HS image and a rigid transformation applied to the MS image. It can handle images with significant scale differences and spatial distortion. The fusion algorithm takes the full high-resolution HS image as an unknown in the objective function. Assuming that the pixels lie on a low-dimensional manifold invariant to local linear transformations from spectral degradation, the fusion optimization problem leads to a closed-form solution. The method was validated on the Pavia University, Salton Sea, and the Mississippi Gulfport datasets. When the proposed registration algorithm is compared to its rigid variant and two mutual information-based methods, it has the best accuracy for both the nonrigid simulated dataset and the real dataset, with an average error less than 0.15 pixels for nonrigid distortion of maximum 1 HS pixel. When the fusion algorithm is compared with current state-of-the-art algorithms, it has the best performance on images with registration errors as well as on simulations that do not consider registration effects. Numéro de notice : A2020-231 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2941494 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2941494 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94969 [article]

Restoration of information obscured by mountainous shadows through Landsat TM/ETM+ images without the use of DEM data : A new method / Yuan Zhou in IEEE Transactions on geoscience and remote sensing, vol 52 n° 1 tome 1 (January 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 1 tome 1 (January 2014) . - pp 313 - 328 Titre : Restoration of information obscured by mountainous shadows through Landsat TM/ETM+ images without the use of DEM data : A new method Type de document : Article/Communication Auteurs : Yuan Zhou, Auteur ; Jin Chen, Auteur ; Qinghua Guo, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 313 - 328 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] image Landsat-ETM+ [Termes IGN] image Landsat-TM [Termes IGN] montagne [Termes IGN] ombre [Termes IGN] pixel [Termes IGN] plus proche voisin, algorithme du [Termes IGN] restauration d'image [Termes IGN] valeur radiométrique Résumé : (Auteur) Shadows in remotely sensed imagery occur when objects totally or partially occlude direct light from a source of illumination, generating great difficulty in land cover interpretation and classification because of the loss of spectral information of shaded pixels. In a mountainous environment with rough terrain, shadows are especially pronounced due to the differentiation of direct illumination between sunny and shady slopes. Topographic correction methods, which are widely used to adjust for differences in solar incidence angles, can partly alleviate the impacts of shadows. However, there are two limitations: one is that the contemporary topographic corrections have little effect on areas that have very low incidence angles and areas that are completely without direct solar illumination (cast shadow); another is that their effectiveness is restricted by the data quality and completeness, spatial resolution, and elevation accuracy of the Digital Elevation Model (DEM) data, which is not currently available in all parts of the world. Thus, noise and errors may be introduced in topographic correction during resampling and geometric registration of the target image. This paper proposes a new approach to restore the radiometric information of mountainous cast shadows using a spectral processing technique called “continuum removal” (CR) without the aid of DEM. The CR-based approach makes full use of the spectral information derived from both the shaded pixels and their neighboring nonshaded pixels of the same land cover type. Several Landsat TM images were used to assess the performance of the proposed method. Results indicated that the proposed method can effectively restore the spectral values of shaded pixels more accurately than the ATCOR_3 correction method, especially for very low incidence angle areas and cast shadows. By comparing data values of shaded pixels with nonshaded pixels (pure reference pixels) of their same class, images processed by the proposed method had the lowest average root mean square error (RMSE) between them in visible, NIR and SWIR bands, followed by the ATCOR_3 correction method and the original image. In addition, the proposed method achieved the best classification accuracy, higher than those from the original test image and the ATCOR_3 corrected image generated using 90 m or 30 m spatial resolution DEM. Therefore, the Continuum Removal method is a better alternative for restoring objects obscured by mountainous shadow when adequate DEM data are unavailable and the quality of DEM cannot satisfy the requirements of topographic correction algorithms. Numéro de notice : A2014-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2239651 En ligne : https://doi.org/10.1109/TGRS.2013.2239651 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32942 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014011A	RAB	Revue	Centre de documentation	En réserve L003	Disponible