Descripteur
Documents disponibles dans cette catégorie (1418)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Substance dependence constrained sparse NMF for hyperspectral unmixing / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)
![]()
[article]
Titre : Substance dependence constrained sparse NMF for hyperspectral unmixing Type de document : Article/Communication Auteurs : Yuan Yuan, Auteur ; Min Fu, Auteur ; Xiaoqiang Lu, Auteur Année de publication : 2015 Article en page(s) : pp 2975 - 2986 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] classification barycentrique
[Termes IGN] état de l'art
[Termes IGN] factorisation
[Termes IGN] factorisation de matrice non-négative
[Termes IGN] image hyperspectrale
[Termes IGN] matrice creuseRésumé : (Auteur) Hyperspectral unmixing is one of the most important problems in analyzing remote sensing images, which aims to decompose a mixed pixel into a collection of constituent materials named endmembers and their corresponding fractional abundances. Recently, various methods have been proposed to incorporate sparse constraints into hyperspectral unmixing and achieve advanced performance. However, most of them ignore the complex distribution of substances in hyperspectral data so that they are only effective in limited cases. In this paper, the concept of substance dependence is introduced to help hyperspectral unmixing. Generally, substance dependence can be considered in a local region by K-nearest neighbors method. However, since substances of hyperspectral images are complicatedly distributed, number K of the most similar substances to each substance is difficult to decide. In this case, substance dependence should be considered in the whole data space, and the number of the K most similar substances to each substance can be adaptively determined by searching from the whole space. Through maintaining the substance dependence during unmixing, the abundances resulted from the proposed method are closer to the real fractions, which lead to better unmixing performance. The following contributions can be summarized. 1) The concept of substance dependence is proposed to describe the complicated relationship between substances in the hyperspectral image. 2) We propose substance dependence constrained sparse nonnegative matrix factorization (SDSNMF) for hyperspectral unmixing. Using SDSNMF, we meet or exceed state-of-the-art unmixing performance. 3) Adequate experiments on both synthetic and real hyperspectral data have been tested. Compared with the state-of-the-art methods, the experimental results prove the superiority of the proposed method. Numéro de notice : A2015-280 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2365953 Date de publication en ligne : 13/01/2015 En ligne : https://doi.org/10.1109/TGRS.2014.2365953 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76391
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 2975 - 2986[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015061 SL Revue Centre de documentation Revues en salle Disponible Very high resolution image matching based on local features and k-means clustering / Amin Sedaghat in Photogrammetric record, vol 30 n° 150 (June - August 2015)
![]()
[article]
Titre : Very high resolution image matching based on local features and k-means clustering Type de document : Article/Communication Auteurs : Amin Sedaghat, Auteur ; Hamid Ebadi, Auteur Année de publication : 2015 Article en page(s) : pp 166 - 186 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] distance euclidienne
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image à très haute résolution
[Termes IGN] partitionnement
[Termes IGN] primitive géométriqueRésumé : (Auteur) Image matching is a critical process in photogrammetry and remote sensing. Automatic and reliable feature matching using well-distributed points in very high resolution images is a difficult task due to significant relief displacement caused by tall buildings and ground relief. In this paper a robust and efficient image-matching approach is proposed, consisting of two main steps. In the first step, three sets of local features – Harris points, UR-SIFT and MSER – are extracted over the entire image. A SIFT (scale-invariant feature transform) descriptor is then created for each extracted feature, and an initial cross-matching verification is performed using the Euclidean distance between feature descriptors. In the second step, an approach based on k-means clustering is performed to achieve accurate matching without mismatched features, followed by a consistency check using a local affine transformation model for each cluster. The proposed method is successfully applied to matching various aerial and satellite images and the results demonstrate its robustness and capability. Numéro de notice : A2015-366 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12101 Date de publication en ligne : 29/06/2015 En ligne : https://doi.org/10.1111/phor.12101 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76908
in Photogrammetric record > vol 30 n° 150 (June - August 2015) . - pp 166 - 186[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 106-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
![]()
[article]
Titre : Complementarity of discriminative classifiers and spectral unmixing techniques for the interpretation of hyperspectral images Type de document : Article/Communication Auteurs : Jun Li, Auteur ; Immaculada Dopido, Auteur ; Paolo Gamba, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2899 - 2912 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification dirigée
[Termes IGN] image hyperspectraleRésumé : (Auteur) Classification and spectral unmixing are two important techniques for hyperspectral data exploitation. Traditionally, these techniques have been exploited independently. In this paper, we propose a new technique that exploits their complementarity. Specifically, we develop a new framework for semisupervised hyperspectral image classification that naturally integrates the information provided by discriminative classification and spectral unmixing. The idea is to assign more confidence to the information provided by discriminative classification for those pixels that can be easily catalogued due to their spectral purity. For those pixels that are more highly mixed in nature, we assign more confidence to the information provided by spectral unmixing. In this case, we use a traditional spectral unmixing chain to produce the abundance fractions of the pure signatures (endmembers) that model the mixture information at a subpixel level. The decision on which source of information is prioritized in the process is taken adaptively, when new unlabeled samples are selected and included in our semisupervised framework. In this regard, the proposed approach can adaptively integrate these two sources of information without the need to establish any weight parameters, thus exploiting the complementarity of classification and unmixing and selecting the most appropriate source of information in each case. In order to test our concept, which has similar computational complexity as traditional semisupervised classification strategies, we have used two different hyperspectral data sets with different characteristics and spatial resolution. In our experiments, we consider two different discriminative classifiers: multinomial logistic regression and probabilistic support vector machine. The obtained results indicate that the proposed approach, which jointly exploits the features provided by classification and spectral unmixing in adaptive fashion, offers an effective solution to improve- classification performance in hyperspectral scenes containing mixed pixels. Numéro de notice : A2015-521 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2366513 En ligne : https://doi.org/10.1109/TGRS.2014.2366513 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77532
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2899 - 2912[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible A critical comparison among pansharpening algorithms / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
![]()
[article]
Titre : A critical comparison among pansharpening algorithms Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Luciano Alparone, Auteur ; Jocelyn Chanussot, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2565 - 2586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de fusion
[Termes IGN] analyse comparative
[Termes IGN] analyse multibande
[Termes IGN] analyse multirésolution
[Termes IGN] état de l'art
[Termes IGN] image à haute résolution
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] image satellite
[Termes IGN] Matlab
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] qualité des donnéesRésumé : (Auteur) Pansharpening aims at fusing a multispectral and a panchromatic image, featuring the result of the processing with the spectral resolution of the former and the spatial resolution of the latter. In the last decades, many algorithms addressing this task have been presented in the literature. However, the lack of universally recognized evaluation criteria, available image data sets for benchmarking, and standardized implementations of the algorithms makes a thorough evaluation and comparison of the different pansharpening techniques difficult to achieve. In this paper, the authors attempt to fill this gap by providing a critical description and extensive comparisons of some of the main state-of-the-art pansharpening methods. In greater details, several pansharpening algorithms belonging to the component substitution or multiresolution analysis families are considered. Such techniques are evaluated through the two main protocols for the assessment of pansharpening results, i.e., based on the full- and reduced-resolution validations. Five data sets acquired by different satellites allow for a detailed comparison of the algorithms, characterization of their performances with respect to the different instruments, and consistency of the two validation procedures. In addition, the implementation of all the pansharpening techniques considered in this paper and the framework used for running the simulations, comprising the two validation procedures and the main assessment indexes, are collected in a MATLAB toolbox that is made available to the community. Numéro de notice : A2015-523 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2361734 En ligne : https://doi.org/10.1109/TGRS.2014.2361734 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77534
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2565 - 2586[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015051 RAB Revue Centre de documentation En réserve L003 Disponible Détermination de la précision planimétrique des images Google Earth haute résolution de Rome (1ère partie) / Guiseppe Pulighe in Géomatique expert, n° 104 (mai - juin 2015)
[article]
Accompagne Détermination de la précision planimétrique des images Google Earth haute-résolution de Rome (2ème partie) / Guiseppe Pulighe in Géomatique expert, n° 105 (juillet - août 2015)
Titre : Détermination de la précision planimétrique des images Google Earth haute résolution de Rome (1ère partie) Type de document : Article/Communication Auteurs : Guiseppe Pulighe, Auteur ; Valerio Baiocchi, Auteur ; Flavio Lupia, Auteur Année de publication : 2015 Article en page(s) : pp 48 - 55 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] données cadastrales
[Termes IGN] données localisées
[Termes IGN] données spatiotemporelles
[Termes IGN] échelle cartographique
[Termes IGN] erreur de positionnement
[Termes IGN] Google Earth
[Termes IGN] image à haute résolution
[Termes IGN] image satellite
[Termes IGN] point géodésique
[Termes IGN] précision planimétrique
[Termes IGN] qualité des données
[Termes IGN] RomeRésumé : (Auteur) Google Earth (GE) est rapidement devenu un globe virtuel très prisé pour certaines études scientifiques, en raison essentiellement de sa couverture globale et de sa gratuité. Toutefois, l'utilisation d'un tel support pose des questions légitimes quant à la qualité des données géospatiales que l'on peut en extraire (précision de calage, absence de déformations, cohérence des clichés aux différentes échelles). Cet article évalue la précision des images Google Earth de la ville de Rome, prises à trois dates différentes. Le test s'est effectué en utilisant soit des points de contrôle homologués par le cadastre italien, soit des points GPS stationnés pour l'occasion. Les résultats montrent une précision au pire métrique, en tout cas suffisante pour dériver des clichés Google Earth des cartes thématiques précises, même à petite échelle. Numéro de notice : A2015-291 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76445
in Géomatique expert > n° 104 (mai - juin 2015) . - pp 48 - 55[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 265-2015031 RAB Revue Centre de documentation En réserve L003 Disponible IFN-001-P001712 PER Revue Nogent-sur-Vernisson Salle périodiques Exclu du prêt Forest species recognition based on dynamic classifier selection and dissimilarity feature vector representation / J.G. Martins in Machine Vision and Applications, vol 26 n° 2-3 (April 2015)
PermalinkHYCA: A new technique for hyperspectral compressive sensing / G. Martin in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
PermalinkHyperspectral image classification based on three-dimensional scattering wavelet transform / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
PermalinkPermalinkMultispectral sensor spectral resolution simulations for generation of hyperspectral vegetation indices from Hyperion data / Prabir Das in Geocarto international, vol 30 n° 5 - 6 (May - July 2015)
PermalinkRefining high spatial resolution remote sensing image segmentation for man-made objects through acollinear and ipsilateral neighborhood model / Min Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 5 (May 2015)
PermalinkSpectral–spatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)
PermalinkActive learning with gaussian process classifier for hyperspectral image classification / Shujing Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkApport du LiDAR dans le géoréférencement d'images hyperspectrales en vue d'un couplage LiDAR/hyperspectral / Antoine Ba in Revue Française de Photogrammétrie et de Télédétection, n° 210 (Avril 2015)
PermalinkClassifying compound structures in satellite images : A compressed representation for fast queries / Lionel Gueguen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkEvaluating leaf chlorophyll content prediction from multispectral remote sensing data within a physically-based modelling framework / H. Croft in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkFast subpixel mapping algorithms for subpixel resolution change detection / Qunming Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkImproving forest aboveground biomass estimation using seasonal Landsat NDVI time-series / Xiaolin Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkLinear spectral mixture analysis via multiple-kernel learning for hyperspectral image classification / Keng-Hao Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkObject-based assessment of burn severity in diseased forests using high-spatial and high-spectral resolution MASTER airborne imagery / Gang Chen in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkA physics-based unmixing method to estimate subpixel temperatures on mixed pixels / Manuel Cubero-Castan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkA technique for simultaneous visualization and segmentation of hyperspectral data / Abhimitra Meka in IEEE Transactions on geoscience and remote sensing, vol 53 n° 4 (April 2015)
PermalinkThe guided bilateral filter: When the joint/cross bilateral filter becomes robust / Laurent Caraffa in IEEE Transactions on image processing, vol 24 n° 4 (April 2015)
PermalinkTraining set size, scale, and features in Geographic Object-Based Image Analysis of very high resolution unmanned aerial vehicle imagery / Lei Ma in ISPRS Journal of photogrammetry and remote sensing, vol 102 (April 2015)
PermalinkRoad marking extraction using a model&data-driven RJ-MCMC / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)
![]()
Permalink