Descripteur
Termes IGN > imagerie > image numérique > image optique > image multibande > image hyperspectrale
image hyperspectraleVoir aussi |
Documents disponibles dans cette catégorie (471)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery / Yanfei Zhong in ISPRS Journal of photogrammetry and remote sensing, vol 96 (October 2014)
[article]
Titre : Adaptive MAP sub-pixel mapping model based on regularization curve for multiple shifted hyperspectral imagery Type de document : Article/Communication Auteurs : Yanfei Zhong, Auteur ; Yunyun Wu, Auteur ; Liangpei Zhang, Auteur ; Xiong Xu, Auteur Année de publication : 2014 Article en page(s) : pp 134 - 148 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] décomposition du pixel
[Termes IGN] image hyperspectraleRésumé : (Auteur) Sub-pixel mapping is a promising technique for producing a spatial distribution map of different categories at the sub-pixel scale by using the fractional abundance image as the input. The traditional sub-pixel mapping algorithms based on single images often have uncertainty due to insufficient contraint of the sub-pixel land-cover patterns within the low-resolution pixels. To improve the sub-pixel mapping accuracy, sub-pixel mapping algorithms based on auxiliary datasets, e.g., multiple shifted images, have been designed, and the maximum a posteriori (MAP) model has been successfully applied to solve the ill-posed sub-pixel mapping problem. However, the regularization parameter is difficult to set properly. In this paper, to avoid a manually defined regularization parameter, and to utilize the complementary information, a novel adaptive MAP sub-pixel mapping model based on regularization curve, namely AMMSSM, is proposed for hyperspectral remote sensing imagery. In AMMSSM, a regularization curve which includes an L-curve or U-curve method is utilized to adaptively select the regularization parameter. In addition, to take the influence of the sub-pixel spatial information into account, three class determination strategies based on a spatial attraction model, a class determination strategy, and a winner-takes-all method are utilized to obtain the final sub-pixel mapping result. The proposed method was applied to three synthetic images and one real hyperspectral image. The experimental results confirm that the AMMSSM algorithm is an effective option for sub-pixel mapping, compared with the traditional sub-pixel mapping method based on a single image and the latest sub-pixel mapping methods based on multiple shifted images. Numéro de notice : A2014-376 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.06.019 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.06.019 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73815
in ISPRS Journal of photogrammetry and remote sensing > vol 96 (October 2014) . - pp 134 - 148[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014101 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing / Mohamed Amine Bendoumi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 2 (October 2014)
[article]
Titre : Hyperspectral image resolution enhancement using high-resolution multispectral image based on spectral unmixing Type de document : Article/Communication Auteurs : Mohamed Amine Bendoumi, Auteur ; Mingyi He, Auteur ; Shaohui Mei, Auteur Année de publication : 2014 Article en page(s) : pp 6574 - 6583 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibandeRésumé : (Auteur) In this paper, a hyperspectral (HS) image resolution enhancement algorithm based on spectral unmixing is proposed for the fusion of the high-spatial-resolution multispectral (MS) image and the low-spatial-resolution HS image (HSI). As a result, a high-spatial-resolution HSI is reconstructed based on the high spectral features of the HSI represented by endmembers and the high spatial features of the MS image represented by abundances. Since the number of endmembers extracted from the MS image cannot exceed the number of bands in least-squares-based spectral unmixing algorithm, large reconstruction errors will occur for the HSI, which degrades the fusion performance of the enhanced HSI. Therefore, in this paper, a novel fusion framework is also proposed by dividing the whole image into several subimages, based on which the performance of the proposed spectral-unmixing-based fusion algorithm can be further improved. Finally, experiments on the Hyperspectral Digital Imagery Collection Experiment and Airborne Visible/Infrared Imaging Spectrometer data demonstrate that the proposed fusion algorithms outperform other famous fusion techniques in both spatial and spectral domains. Numéro de notice : A2014-485 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2298056 En ligne : https://doi.org/10.1109/TGRS.2014.2298056 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74684
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 10 tome 2 (October 2014) . - pp 6574 - 6583[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014101B RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes / Aniruddha Ghosh in ISPRS Journal of photogrammetry and remote sensing, vol 96 (October 2014)
[article]
Titre : Hyperspectral imagery for disaggregation of land surface temperature with selected regression algorithms over different land use land cover scenes Type de document : Article/Communication Auteurs : Aniruddha Ghosh, Auteur ; P.K. Joshi, Auteur Année de publication : 2014 Article en page(s) : pp 76 - 93 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] image hyperspectrale
[Termes IGN] occupation du sol
[Termes IGN] régression
[Termes IGN] température de surfaceRésumé : (Auteur)Land surface temperature (LST), a key parameter in understanding thermal behavior of various terrestrial processes, changes rapidly and hence mapping and modeling its spatio-temporal evolution requires measurements at frequent intervals and finer resolutions. We designed a series of experiments for disaggregation of LST (DLST) derived from the Landsat ETM + thermal band using narrowband reflectance information derived from the EO1-Hyperion hyperspectral sensor and selected regression algorithms over three geographic locations with different climate and land use land cover (LULC) characteristics. The regression algorithms applied to this end were: partial least square regression (PLS), gradient boosting machine (GBM) and support vector machine (SVM). To understand the scale dependence of regression algorithms for predicting LST, we developed individual models (local models) at four spatial resolutions (480 m, 240 m, 120 m and 60 m) and tested the differences between these using RMSE derived from cross-validated samples. The sharpening capabilities of the models were assessed by predicting LST at finer resolutions using models developed at coarser spatial resolution. The results were also compared with LST produced by DisTrad sharpening model. It was found that scale dependence of the models is a function of the study area characteristics and regression algorithms. Considering the sharpening experiments, both GBM and SVM performed better than PLS which produced noisy LST at finer spatial resolutions. Based on the results, it can be concluded that GBM and SVM are more suitable algorithms for operational implementation of this application. These algorithms outperformed DisTrad model for heterogeneous landscapes with high variation in soil moisture content and photosynthetic activities. The variable importance measure derived from PLS and GBM provided insights about the characteristics of the relevant bands. The results indicate that wavelengths centered around 457, 671, 1488 and 2013–2083 nm are the most important in predicting LST. Nevertheless, further research is needed to improve the performance of regression algorithms when there is a large variability in LST and to examine the utility of narrowband vegetation indices to predict the LST. The benefits of this research may extend to applications such as monitoring urban heat island effect, volcanic activity and wildfire, estimating evapotranspiration and assessing drought severity. Numéro de notice : A2014-385 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.07.003 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.07.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73812
in ISPRS Journal of photogrammetry and remote sensing > vol 96 (October 2014) . - pp 76 - 93[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2014101 RAB Revue Centre de documentation En réserve L003 Disponible Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation / Davood Akbari in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 10 (October 2014)
[article]
Titre : Object-based hyperspectral classification of urban areas using marker-based hierarchical segmentation Type de document : Article/Communication Auteurs : Davood Akbari, Auteur ; Abdolreza Safari, Auteur ; Saeid Homayouni, Auteur Année de publication : 2014 Article en page(s) : pp 963 - 970 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification orientée objet
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation hiérarchique
[Termes IGN] zone urbaineRésumé : (auteur)An effective approach to spectral-spatial classification has been achieved using Hierarchical SEGmentation (HSEG) by Tarabalka et al. (2009 and 2010). Our goal is to improve this approach to the classification of hyperspectral images in urban areas. The first step of our proposed method is to segment the spectral images using a novel marker-based HSEG, method. The spatial features from segmented images are then extracted. Spatial information such as the area, entropy, shape, adjacency, and relation features constitute the components of feature space. Last, using both spectral and spatial features, the image objects are classified by a support vector machine (SVM) classifier. Three image data-sets were used to test this method. The results of our experiment indicate that the main advantage of the proposed method, compared to the previous HSEG-based approach, is that it increases classification accuracy by selecting the appropriate contextual features of different objects. Numéro de notice : A2014-673 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.10.963 En ligne : https://doi.org/10.14358/PERS.80.10.963 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75153
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 10 (October 2014) . - pp 963 - 970[article]Classification of submerged aquatic vegetation in Black River using hyperspectral image analysis / Roshan Pande-Chhetri in Geomatica, vol 68 n° 3 (September 2014)
[article]
Titre : Classification of submerged aquatic vegetation in Black River using hyperspectral image analysis Type de document : Article/Communication Auteurs : Roshan Pande-Chhetri, Auteur ; Amr Abd-Elrahman, Auteur ; Charles Jacoby, Auteur Année de publication : 2014 Article en page(s) : pp 169 - 182 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur paramétrique
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par réseau neuronal
[Termes IGN] image hyperspectrale
[Termes IGN] macrophyte
[Termes IGN] profondeur
[Termes IGN] réflexion (rayonnement)
[Termes IGN] surface de l'eauRésumé : (Auteur) Le contrôle de la végétation aquatique est un élément important de la gestion des ressources en eau en raison des services écologiques rendus par ces habitats. L'imagerie hyperspectrale dense sur le plan spectral peut être un outil efficace pour cartographier et classifier les communautés macrophytes. L'identification de la végétation submergée dans les régions aquatiques est compliquée par les variations des propriétés optiques des constituants de l'eau, de la géométrie des capteurs d'eau et d'ensoleillement, de la profondeur de l'eau et de la complexité spectrale/structurale des plantes. Plusieurs études ont tenté de détecter la végétation aquatique dans les eaux côtières; mais peu d’études ont ciblé des rivières peu profondes aux eaux noires teintées contaminées par des matières organiques dissoutes du groupe chromophore (CDOM). La présente étude examine les méthodes pour analyser l'imagerie hyperspectrale aéroportée et pour détecter et classifier la végétation aquatique dans un système fluvial d'eaux noires. Les images ont été normalisées afin de tenir compte de la réflexion de la surface de l'eau et de la profondeur changeante de l'eau avant leur analyse par le classificateur à vraisemblance maximale (ML) et trois autres classificateurs non paramétriques: le réseau de neurones formels (ANN), la machine à vecteurs de support (SVM) et un appareil de cartographie angulaire spectral (SAM). L'analyse de l’évaluation de la qualité a indiqué une amélioration générale de la détection et de la classification lorsque les classificateurs non paramétriques étaient appliqués aux images normalisées et à profondeur constante. Une précision maximale de classification d'environ 69% a été atteinte lorsque le classificateur ANN était appliqué aux images normalisées et des précisions maximales de détection de 93% et de 92% ont été atteintes lorsque les classificateurs SAM et SVM étaient appliqués aux images à profondeur constante, respectivement. Numéro de notice : A2014-621 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5623/cig2014-302 En ligne : https://doi.org/10.5623/cig2014-302 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74999
in Geomatica > vol 68 n° 3 (September 2014) . - pp 169 - 182[article]Regularized simultaneous forward–backward greedy algorithm for sparse unmixing of hyperspectral data / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)PermalinkSpectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery / L. Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)PermalinkTélédétection multi-échelle des lacs depuis un aéronef ultraléger motorisé / Y. Akhtman in Géomatique suisse, vol 112 n° 9 (septembre 2014)PermalinkAutomated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor / Helge Aasen in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)PermalinkCombining hyperspectral and Lidar data for vegetation mapping in the Florida Everglades / Caiyun Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)PermalinkDeriving Predictive relationships of carotenoid content at the canopy level in a conifer forest using hyperspectral imagery and model simulation / Rocío Hernández-Clemente in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)PermalinkHyperspectral data dimensionality reduction and the impact of multi-seasonal Hyperion EO-1 imagery on classification accuracies of tropical forest species / Manjit Saini in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)PermalinkHyperspectral remote sensing image subpixel target detection based on supervised metric learning / Lefei Zhang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)PermalinkImproved capability in stone pine forest mapping and management in Lebanon using hyperspectral CHTIS-Proba data relative to Landsat ETM+ / Mohamad Awad in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)PermalinkKernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features / Z. He in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)Permalink