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An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery / Yanfei Zhong in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
[article]
Titre : An adaptive subpixel mapping method based on MAP model and class determination strategy for hyperspectral remote sensing imagery Type de document : Article/Communication Auteurs : Yanfei Zhong, Auteur ; Yunyun Wu, Auteur ; Xiong Xu, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1411 - 1426 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse infrapixellaire
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] classification du maximum a posteriori
[Termes IGN] classification pixellaire
[Termes IGN] image hyperspectrale
[Termes IGN] traitement automatique d'images
[Termes IGN] traitement de données localiséesRésumé : (Auteur) The subpixel mapping technique can specify the spatial distribution of different categories at the subpixel scale by converting the abundance map into a higher resolution image, based on the assumption of spatial dependence. Traditional subpixel mapping algorithms only utilize the low-resolution image obtained by the classification image downsampling and do not consider the spectral unmixing error, which is difficult to account for in real applications. In this paper, to improve the accuracy of the subpixel mapping, an adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery. In AMCDSM, to better simulate a real remote sensing scene, the low-resolution abundance images are obtained by the spectral unmixing method from the downsampled original image or real low-resolution images. The MAP model is extended by considering the spatial prior models (Laplacian, total variation (TV), and bilateral TV) to obtain the high-resolution subpixel distribution map. To avoid the setting of the regularization parameter, an adaptive parameter selection method is designed to acquire the optimal subpixel mapping results. In addition, in AMCDSM, to take into account the spectral unmixing error in real applications, a winner-take-all strategy is proposed to achieve a better subpixel mapping result. The proposed method was tested on simulated, synthetic, and real hyperspectral images, and the experimental results demonstrate that the AMCDSM algorithm outperforms the traditional subpixel mapping methods and provides a simple and efficient algorithm to regularize the ill-posed subpixel mapping problem. Numéro de notice : A2015-132 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2340734 Date de publication en ligne : 07/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2340734 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75796
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1411 - 1426[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Collaborative representation for hyperspectral anomaly detection / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
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Titre : Collaborative representation for hyperspectral anomaly detection Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Qian Du, Auteur Année de publication : 2015 Article en page(s) : pp 1463 - 1474 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification pixellaire
[Termes IGN] détection d'anomalie
[Termes IGN] distance pondérée
[Termes IGN] image hyperspectrale
[Termes IGN] matrice
[Termes IGN] optimisation (mathématiques)
[Termes IGN] plus proche voisin, algorithme duRésumé : (Auteur) In this paper, collaborative representation is proposed for anomaly detection in hyperspectral imagery. The algorithm is directly based on the concept that each pixel in background can be approximately represented by its spatial neighborhoods, while anomalies cannot. The representation is assumed to be the linear combination of neighboring pixels, and the collaboration of representation is reinforced by l2-norm minimization of the representation weight vector. To adjust the contribution of each neighboring pixel, a distance-weighted regularization matrix is included in the optimization problem, which has a simple and closed-form solution. By imposing the sum-to-one constraint to the weight vector, the stability of the solution can be enhanced. The major advantage of the proposed algorithm is the capability of adaptively modeling the background even when anomalous pixels are involved. A kernel extension of the proposed approach is also studied. Experimental results indicate that our proposed detector may outperform the traditional detection methods such as the classic Reed-Xiaoli (RX) algorithm, the kernel RX algorithm, and the state-of-the-art robust principal component analysis based and sparse-representation-based anomaly detectors, with low computational cost. Numéro de notice : A2015-133 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2343955 Date de publication en ligne : 12/08/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2343955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75799
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1463 - 1474[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Constrained least squares algorithms for nonlinear unmixing of hyperspectral imagery / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)
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Titre : Constrained least squares algorithms for nonlinear unmixing of hyperspectral imagery Type de document : Article/Communication Auteurs : Hanye Pu, Auteur ; Zhao Chen, Auteur ; Bin Wang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 1287 - 1303 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] contrainte d'intégrité
[Termes IGN] image hyperspectrale
[Termes IGN] méthode des moindres carrésRésumé : (Auteur) Hyperspectral unmixing is an important issue in hyperspectral image processing. In this paper, we transform the unmixing problem into a constrained nonlinear least squares (CNLS) problem by introducing the abundance sum-to-one constraint, abundance nonnegative constraint, and bound constraints on nonlinearity parameters. The new CNLS-based algorithms assume that the mixing mechanism of each observed pixel can be described by two forms. One is a sum of linear mixtures of endmember spectra and nonlinear variations in reflectance, and the other is a joint mixture resulting from the linearity and nonlinearity in hyperspectral data. For the former, an alternating iterative optimization algorithm is developed to solve the problem of CNLS. As for the latter, the structured total least squares optimization approach is used to obtain the abundance vectors and nonlinearity parameters simultaneously. Current mixing models can be interpreted by either or both of these two mechanisms. A comparative analysis based on Monte Carlo simulations and real data experiments is conducted to evaluate the proposed algorithms and five other state-of-the-art algorithms. Experimental results show that the proposed algorithms give outstanding performance of hyperspectral nonlinear unmixing for both synthetic data and real hyperspectral images, as satisfactory accuracy in term of abundance fractions and low computational complexity are observed. Numéro de notice : A2015-131 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2336858 Date de publication en ligne : 30/07/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2336858 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75794
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1287 - 1303[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2015031 RAB Revue Centre de documentation En réserve L003 Disponible Employing ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland / Samuel Adelabu in Geocarto international, vol 30 n° 3 - 4 (March - April 2015)
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Titre : Employing ground and satellite-based QuickBird data and Random forest to discriminate five tree species in a Southern African Woodland Type de document : Article/Communication Auteurs : Samuel Adelabu, Auteur ; Timothy Dube, Auteur Année de publication : 2015 Article en page(s) : pp 457 - 471 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] Afrique du sud (état)
[Termes IGN] analyse diachronique
[Termes IGN] Botswana
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données de terrain
[Termes IGN] espèce végétale
[Termes IGN] forêt
[Termes IGN] image hyperspectrale
[Termes IGN] image Quickbird
[Termes IGN] rééchantillonnage
[Termes IGN] réflectance végétale
[Termes IGN] savaneRésumé : (Auteur) With the emergence of very high spatial and spectral resolution data set, the resolution gap that existed between remote-sensing data set and aerial photographs has decreased. The decrease in resolution gap has allowed accurate discrimination of different tree species. In this study, discrimination of indigenous tree species (n = 5) was carried out using ground based hyperspectral data resampled to QuickBird bands and the actual QuickBird imagery for the area around Palapye, Botswana. The purpose of the study was to compare the accuracies of resampled hyperspectral data (resampled to QuickBird sensors) with the actual image (QuickBird image) in discriminating between the indigenous tree species. We performed Random Forest (RF) using canopy reflectance taking from ground-based hyperspectral sensor and the reflectance delineated regions of the tree species. The overall accuracies for classifying the five tree species was 79.86 and 88.78% for both the resampled and actual image, respectively. We observed that resampled data set can be upscale to actual image with the same or even greater level of accuracy. We therefore conclude that high spectral and spatial resolution data set has substantial potential for tree species discrimination in savannah environments. Numéro de notice : A2015-306 Affiliation des auteurs : non IGN Thématique : FORET/GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2014.885589 Date de publication en ligne : 31/03/2014 En ligne : https://doi.org/10.1080/10106049.2014.885589 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76524
in Geocarto international > vol 30 n° 3 - 4 (March - April 2015) . - pp 457 - 471[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 059-2015021 RAB Revue Centre de documentation En réserve L003 Disponible Les journées de la recherche 2015 à l'IGN / Anonyme in Géomatique expert, n° 103 (mars - avril 2015)
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Titre : Les journées de la recherche 2015 à l'IGN Type de document : Article/Communication Auteurs : Anonyme, Auteur Année de publication : 2015 Article en page(s) : pp 4 - 9 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] acquisition d'images
[Termes IGN] agriculture de précision
[Termes IGN] drone
[Termes IGN] image hyperspectrale
[Termes IGN] sylvicultureRésumé : (Editeur) Comme chaque année à la fin de l'hiver, l'IGN organisait ses "Journées de la recherche" pour donner la parole aux chercheurs des différents laboratoires afin qu'ils exposent le but et l'état de leurs travaux. Numéro de notice : A2015-125 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75779
in Géomatique expert > n° 103 (mars - avril 2015) . - pp 4 - 9[article]Exemplaires(2)
Code-barres Cote Support Localisation Section Disponibilité 265-2015021 RAB Revue Centre de documentation En réserve L003 Disponible IFN-001-P001689 GEO Revue Nogent-sur-Vernisson Salle périodiques Disponible Semisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization / Zhangyang Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkSupervised spectral–spatial hyperspectral image classification with weighted markov random fields / Le Sun in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)PermalinkCoregistration refinement of hyperspectral images and DSM: An object-based approach using spectral information / Janja Avbelj in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)PermalinkGabor feature-based collaborative representation for hyperspectral imagery classification / Sen Jia in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)Permalinkvol 100 - February 2015 - High-resolution Earth imaging for geospatial information (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Christian HeipkePermalinkHyperspectral Band Selection by Multitask Sparsity Pursuit / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)PermalinkMeasurement of ground displacement from optical satellite image correlation using the free open-source software MicMac / Ana-Maria Rosu in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)PermalinkPersistent scatterers at building facades – Evaluation of appearance and localization accuracy / Stefan Gernhardt in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)PermalinkSparse unmixing of hyperspectral data using spectral a priori information / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)PermalinkAn abundance characteristic-based independent component analysis for hyperspectral unmixing / Nan Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkAutomatic spatial–spectral feature selection for hyperspectral image via discriminative sparse multimodal learning / Qian Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkCorrection of distorsions in YG-12 high-resolution panchromatic images / Yonghua Jiang in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 1 (January 2015)PermalinkEssential Earth imaging for GIS / Lawrence Fox III (2015)PermalinkEstimation of the mean tree height of forest stands by photogrammetric measurement using digital aerial images of high spatial resolution / Ivan Balenović in Annals of forest research, vol 58 n° 1 (January 2015)PermalinkEtude expérimentale en cartographie de la végétation par télédétection / Vanessa Sellin in Cybergeo, European journal of geography, n° 2015 ([01/01/2015])PermalinkExtended random walker-based classification of hyperspectral images / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkExterior orientation of hyperspectral frame images collected with UAV for forest applications / Adilson Berveglieri (2015)PermalinkExtraction of optimal spectral bands using hierarchical band merging out of hyperspectral data / Arnaud Le Bris (2015)PermalinkHierarchical unsupervised change detection in multitemporal hyperspectral images / S. Liu in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)PermalinkHyperspectral image denoising via sparse representation and low-rank constraint / Yong-Qiang Zhao in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)Permalink