Descripteur
Termes IGN > imagerie > image numérique > image optique > image multibande
image multibandeSynonyme(s)Image xs ;Image multispectrale donnees multispectralesVoir aussi |
Documents disponibles dans cette catégorie (984)
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
Superpixel-based graphical model for remote sensing image mapping / Guangyun Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 11 (November 2015)
[article]
Titre : Superpixel-based graphical model for remote sensing image mapping Type de document : Article/Communication Auteurs : Guangyun Zhang, Auteur ; Xiuping Jia, Auteur ; Jiankun Hu, Auteur Année de publication : 2015 Article en page(s) : pp 5861 - 5871 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification contextuelle
[Termes IGN] classification pixellaire
[Termes IGN] décomposition du pixel
[Termes IGN] image multibande
[Termes IGN] modèle sémantique de données
[Termes IGN] segmentation d'imageRésumé : (Auteur) Object-oriented remote sensing image classification is becoming more and more popular because it can integrate spatial information from neighboring regions of different shapes and sizes into the classification procedure to improve the mapping accuracy. However, object identification itself is difficult and challenging. Superpixels, which are groups of spatially connected similar pixels, have the scale between the pixel level and the object level and can be generated from oversegmentation. In this paper, we establish a new classification framework using a superpixel-based graphical model. Superpixels instead of pixels are applied as the basic unit to the graphical model to capture the contextual information and the spatial dependence between the superpixels. The advantage of this treatment is that it makes the classification less sensitive to noise and segmentation scale. The contribution of this paper is the application of a graphical model to remote sensing image semantic segmentation. It is threefold. 1) Gradient fusion is applied to multispectral images before the watershed segmentation algorithm is used for superpixel generation. 2) A probabilistic fusion method is designed to derive node potential in the superpixel-based graphical model to address the problem of insufficient training samples at the superpixel level. 3) A boundary penalty between the superpixels is introduced in the edge potential evaluation. Experiments on three real data sets were conducted. The results show that the proposed method performs better than the related state-of-the-art methods tested. Numéro de notice : A2015-770 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2423688 Date de publication en ligne : 08/06/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2423688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78826
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 11 (November 2015) . - pp 5861 - 5871[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015111 SL Revue Centre de documentation Revues en salle Disponible Efficient superpixel-level multitask joint sparse representation for hyperspectral image classification / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)
[article]
Titre : Efficient superpixel-level multitask joint sparse representation for hyperspectral image classification Type de document : Article/Communication Auteurs : Jiayi Li, Auteur ; Hongyan Zhang, Auteur ; Liangpei Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 5338 - 5351 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse infrapixellaire
[Termes IGN] apprentissage automatique
[Termes IGN] classification
[Termes IGN] données clairsemées
[Termes IGN] image hyperspectrale
[Termes IGN] représentation des donnéesRésumé : (Auteur) In this paper, we propose a superpixel-level sparse representation classification framework with multitask learning for hyperspectral imagery. The proposed algorithm exploits the class-level sparsity prior for multiple-feature fusion, and the correlation and distinctiveness of pixels in a spatial local region. Compared with some of the state-of-the-art hyperspectral classifiers, the superiority of the multiple-feature combination, the spatial prior utilization, and the computational complexity are maintained at the same time in the proposed method. The proposed classification algorithm was tested on three hyperspectral images. The experimental results suggest that the proposed algorithm performs better than the other sparse (collaborative) representation-based algorithms and some popular hyperspectral multiple-feature classifiers. Numéro de notice : A2015-749 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421638 Date de publication en ligne : 29/04/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421638 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78758
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5338 - 5351[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015101 SL Revue Centre de documentation Revues en salle Disponible Fusion of waveform LiDAR data and hyperspectral imagery for land cover classification / Hongzhou Wang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)
[article]
Titre : Fusion of waveform LiDAR data and hyperspectral imagery for land cover classification Type de document : Article/Communication Auteurs : Hongzhou Wang, Auteur ; Craig L. Glennie, Auteur Année de publication : 2015 Article en page(s) : pp 1 - 11 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] forme d'onde pleine
[Termes IGN] fusion d'images
[Termes IGN] fusion de données
[Termes IGN] image hyperspectrale
[Termes IGN] occupation du sol
[Termes IGN] onde lidar
[Termes IGN] semis de points
[Termes IGN] superposition d'images
[Termes IGN] voxelRésumé : (auteur) Current research into the fusion of hyperspectral imagery (HI) and full waveform LiDAR (Light Detection And Ranging) has relied on first processing the full waveform LiDAR (FWL) data to a set of discrete returns before merging because the data structure and sampling interval of HI and FWL are distinctly different. However, additional information about target properties can potentially be recovered if the waveform shape is preserved in the fusion process. This paper proposes a “voxelization” method to register FWL data to HI by dividing the waveform data into voxels, and then synthesizing all waveforms which intersect a voxel column into one three-dimensional superposition waveform: the synthesized waveform (SWF). A vertical energy distribution coefficients (VEDC) feature is proposed for extracting features from SWF, and then the SWF and HI are fused to form a complete feature space for classification. A pairwise classifier was adapted and completed using both Maximum Likelihood and Support Vector Machine classifiers for the combined SWF/HI features. Results show that this method of generating SWF from FWL data can effectively preserve information from the original waveforms, and the fusion of SWF and HI enhanced land cover classification compared to both using either data set alone or the merging of HI with a discrete LiDAR return point cloud. Numéro de notice : A2015-848 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.05.012 Date de publication en ligne : 23/06/2015 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.05.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79218
in ISPRS Journal of photogrammetry and remote sensing > vol 108 (October 2015) . - pp 1 - 11[article]Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA / Brian D. Bue in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)
[article]
Titre : Leveraging in-scene spectra for vegetation species discrimination with MESMA-MDA Type de document : Article/Communication Auteurs : Brian D. Bue, Auteur ; David R. Thompson, Auteur ; R. Glenn Sellar, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 33 - 48 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de mélange spectral d’extrémités multiples
[Termes IGN] analyse discriminante
[Termes IGN] espèce végétale
[Termes IGN] image hyperspectrale
[Termes IGN] réflectance végétale
[Termes IGN] signature spectrale
[Termes IGN] spectromètre imageurRésumé : (auteur) We describe an approach to improve Multiple Endmember Spectral Mixture Analysis (MESMA) results for applications involving discrimination among spectrally-similar species, and commonly occur in multispectral and hyperspectral vegetation remote sensing studies. Such applications are inherently difficult, due to the high degree of similarity between distinct species, coupled with potentially high intra-species variability caused by factors such as growing conditions, canopy structure, ambient illumination, or substrate characteristics. We describe a method to map spectra to a feature space where distinctions between plant species are emphasized using a transformation based on Multiclass Discriminant Analysis. We compute this transformation using groups of pixels that represent individual plant canopies similar to the endmembers in MESMA’s spectral library, and describe a technique to automatically select such spectra from a given image. Compared to conventional MESMA, and also to several alternative MESMA formulations, we observe up to twofold increases in accuracy, along with a factor of ten reduction in computation time using our MESMA approach in several species discrimination applications. We demonstrate the effectiveness of our approach for agricultural species discrimination applications using spectra captured by two different imaging spectrometers. Numéro de notice : A2015-850 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.06.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79220
in ISPRS Journal of photogrammetry and remote sensing > vol 108 (October 2015) . - pp 33 - 48[article]A novel MKL model of integrating LiDAR data and MSI for urban area classification / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)
[article]
Titre : A novel MKL model of integrating LiDAR data and MSI for urban area classification Type de document : Article/Communication Auteurs : Yanfeng Gu, Auteur ; Qingwang Wang, Auteur ; Xiuping Jia, Auteur ; Jón Alti, Auteur Année de publication : 2015 Article en page(s) : pp 5312 - 5326 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] classificateur
[Termes IGN] classification à base de connaissances
[Termes IGN] classification automatique
[Termes IGN] données lidar
[Termes IGN] image multibande
[Termes IGN] image spectrale
[Termes IGN] milieu urbainRésumé : (Auteur) A novel multiple-kernel learning (MKL) model is proposed for urban classification to integrate heterogeneous features (HF-MKL) from two data sources, i.e., spectral images and LiDAR data. The features include spectral, spatial, and elevation attributes of urban objects from the two data sources. With these heterogeneous features (HFs), the new MKL model is designed to carry out feature fusion that is embedded in classification. First, Gaussian kernels with different bandwidths are used to measure the similarity of samples on each feature at different scales. Then, these multiscale kernels with different features are integrated using a linear combination. In the combination, the weights of the kernels with different features are determined by finding a projection based on the maximum variance. This way, the discriminative ability of the HFs is exploited at different scales and is also integrated to generate an optimal combined kernel. Finally, the optimization of the conventional support vector machine with this kernel is performed to construct a more effective classifier. Experiments are conducted on two real data sets, and the experimental results show that the HF-MKL model achieves the best performance in terms of classification accuracies in integrating the HFs for classification when compared with several state-of-the-art algorithms. Numéro de notice : A2015-752 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2421051 Date de publication en ligne : 07/05/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2421051 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78742
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 10 (October 2015) . - pp 5312 - 5326[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015101 SL Revue Centre de documentation Revues en salle Disponible On diverse noises in hyperspectral unmixing / Chunzhi Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkTwo dimensional linear discriminant analyses for hyperspectral data / Maryam Imani in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 10 (October 2015)PermalinkMinimum volume simplex analysis: A fast algorithm for linear hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkRegion-kernel-based support vector machines for hyperspectral image classification / Jiangtao Peng in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkTélédétection pour l'agriculture de précision par caméra hyperspectrale miniature / D. Constantin in Géomatique suisse, vol 113 n° 9 (septembre 2015)PermalinkAn unsupervised urban change detection procedure by using luminance and saturation for multispectral remotely sensed images / Su Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 8 (August 2015)PermalinkA local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery / F. Cánovas-García in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)PermalinkSequential spectral change vector analysis for iteratively discovering and detecting multiple changes in hyperspectral images / Sicong Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)PermalinkSpectral–spatial classification of hyperspectral images with a superpixel-based discriminative sparse model / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 8 (August 2015)PermalinkTesting the reliability and stability of the internal accuracy assessment of random forest for classifying tree defoliation levels using different validation methods / Samuel Adelabu in Geocarto international, vol 30 n° 7 - 8 (August - September 2015)Permalink