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image multibandeSynonyme(s)Image xs ;Image multispectrale donnees multispectralesVoir aussi |
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Hyperspectral image noise reduction based on rank-1 tensor decomposition / Xian Guoa in ISPRS Journal of photogrammetry and remote sensing, vol 83 (September 2013)
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Titre : Hyperspectral image noise reduction based on rank-1 tensor decomposition Type de document : Article/Communication Auteurs : Xian Guoa, Auteur ; Xian Guo, Auteur ; Xin Huang, Auteur ; Liangpei Zhanga, Auteur ; Lefei Zhang, Auteur Année de publication : 2013 Article en page(s) : pp 50 - 63 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] calcul tensoriel
[Termes IGN] décomposition spatiale
[Termes IGN] filtrage du bruit
[Termes IGN] image hyperspectrale
[Termes IGN] tenseur
[Termes IGN] valeur propre
[Termes IGN] voxelRésumé : (Auteur) In this study, a novel noise reduction algorithm for hyperspectral imagery (HSI) is proposed based on high-order rank-1 tensor decomposition. The hyperspectral data cube is considered as a three-order tensor that is able to jointly treat both the spatial and spectral modes. Subsequently, the rank-1 tensor decomposition (R1TD) algorithm is applied to the tensor data, which takes into account both the spatial and spectral information of the hyperspectral data cube. A noise-reduced hyperspectral image is then obtained by combining the rank-1 tensors using an eigenvalue intensity sorting and reconstruction technique. Compared with the existing noise reduction methods such as the conventional channel-by-channel approaches and the recently developed multidimensional filter, the spatial–spectral adaptive total variation filter, experiments with both synthetic noisy data and real HSI data reveal that the proposed R1TD algorithm significantly improves the HSI data quality in terms of both visual inspection and image quality indices. The subsequent image classification results further validate the effectiveness of the proposed HSI noise reduction algorithm. Numéro de notice : A2013-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.06.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.06.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32626
in ISPRS Journal of photogrammetry and remote sensing > vol 83 (September 2013) . - pp 50 - 63[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013091 RAB Revue Centre de documentation En réserve L003 Disponible Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data / Abel Ramoelo in ISPRS Journal of photogrammetry and remote sensing, vol 82 (August 2013)
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Titre : Non-linear partial least square regression increases the estimation accuracy of grass nitrogen and phosphorus using in situ hyperspectral and environmental data Type de document : Article/Communication Auteurs : Abel Ramoelo, Auteur ; Andrew K. Skidmore, Auteur ; Moses Azong Cho, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 27 - 40 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Afrique du sud (état)
[Termes IGN] azote
[Termes IGN] données environnementales
[Termes IGN] herbe
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] parc naturel national
[Termes IGN] parcours
[Termes IGN] phosphore
[Termes IGN] régression non linéaire
[Termes IGN] savaneRésumé : (Auteur) Grass nitrogen (N) and phosphorus (P) concentrations are direct indicators of rangeland quality and provide imperative information for sound management of wildlife and livestock. It is challenging to estimate grass N and P concentrations using remote sensing in the savanna ecosystems. These areas are diverse and heterogeneous in soil and plant moisture, soil nutrients, grazing pressures, and human activities. The objective of the study is to test the performance of non-linear partial least squares regression (PLSR) for predicting grass N and P concentrations through integrating in situ hyperspectral remote sensing and environmental variables (climatic, edaphic and topographic). Data were collected along a land use gradient in the greater Kruger National Park region. The data consisted of: (i) in situ-measured hyperspectral spectra, (ii) environmental variables and measured grass N and P concentrations. The hyperspectral variables included published starch, N and protein spectral absorption features, red edge position, narrow-band indices such as simple ratio (SR) and normalized difference vegetation index (NDVI). The results of the non-linear PLSR were compared to those of conventional linear PLSR. Using non-linear PLSR, integrating in situ hyperspectral and environmental variables yielded the highest grass N and P estimation accuracy (R2 = 0.81, root mean square error (RMSE) = 0.08, and R2 = 0.80, RMSE = 0.03, respectively) as compared to using remote sensing variables only, and conventional PLSR. The study demonstrates the importance of an integrated modeling approach for estimating grass quality which is a crucial effort towards effective management and planning of protected and communal savanna ecosystems. Numéro de notice : A2013-409 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.04.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.04.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32547
in ISPRS Journal of photogrammetry and remote sensing > vol 82 (August 2013) . - pp 27 - 40[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2013081 RAB Revue Centre de documentation En réserve L003 Disponible Using hyperspectral reflectance data to assess biocontrol damage of giant salvinia / James H. Everitt in Geocarto international, vol 28 n° 5-6 (August - October 2013)
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Titre : Using hyperspectral reflectance data to assess biocontrol damage of giant salvinia Type de document : Article/Communication Auteurs : James H. Everitt, Auteur ; Chenghai Yang, Auteur ; Julie G. Nachtrieb, Auteur Année de publication : 2013 Article en page(s) : pp 502 - 516 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse diachronique
[Termes IGN] analyse discriminante
[Termes IGN] espèce exotique envahissante
[Termes IGN] Etats-Unis
[Termes IGN] image hyperspectrale
[Termes IGN] lutte biologique
[Termes IGN] milieu naturel
[Termes IGN] plante aquatique d'eau salée
[Termes IGN] réflectance végétale
[Termes IGN] surveillance de la végétationMots-clés libres : Salvinia molesta Résumé : (Auteur) Field hyperspectral reflectance data were studied at 50 wavebands (10-nm bandwidth) over the 400- to 900-nm spectral range to determine their potential for distinguishing among giant salvinia (Salvinia molesta Mitchell) plants subjected to four population levels of salvinia weevils (Cyrtobagous salviniae Calder and Sands) to develop feeding damage to the plants. The four populations included a control with no insects and those with low, medium and high insect populations. The plants were studied in two experiments on each of two dates: 14 October 2010 and 21 July 2011. Two procedures were used to determine the optimum bands for discriminating among treatments: least significant difference (LSD) and stepwise discriminant analysis. The LSD comparison test results for both October and July experiments showed that generally the best bands for separating among treatments occurred in the green (505–595 nm), red (605–635 nm), red-near-infrared (NIR; 695–745 nm) edge and NIR (755–895 nm) regions where three to four treatments could be distinguished. Stepwise discriminant analysis identified four bands in the green, red and red-NIR edge to be significant to discriminate among the four treatments in Experiment 1 in October. For Experiment 2 in October, discriminant analysis identified five bands in the blue, green, red and NIR regions to be significant for distinguishing among the treatments. In Experiment 1 in July, five bands in the blue, green, red-NIR edge and NIR regions were found to be significant to discriminate among the treatments. For Experiment 2 in July, discriminant analysis identified four bands in the blue, green and red-NIR edge regions to be significant to discriminate among the treatments. Numéro de notice : A2013-550 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.724454 Date de publication en ligne : 25/09/2012 En ligne : https://doi.org/10.1080/10106049.2012.724454 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78074
in Geocarto international > vol 28 n° 5-6 (August - October 2013) . - pp 502 - 516[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2013031 RAB Revue Centre de documentation En réserve L003 Disponible Deblurring and sparse unmixing for hyperspectral images / Xi-Le Zhao in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
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Titre : Deblurring and sparse unmixing for hyperspectral images Type de document : Article/Communication Auteurs : Xi-Le Zhao, Auteur ; Fan Wang, Auteur ; Ting-Zhu Huang, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4045 - 4058 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] correction d'image
[Termes IGN] flou
[Termes IGN] image hyperspectraleRésumé : (Auteur) The main aim of this paper is to study total variation (TV) regularization in deblurring and sparse unmixing of hyperspectral images. In the model, we also incorporate blurring operators for dealing with blurring effects, particularly blurring operators for hyperspectral imaging whose point spread functions are generally system dependent and formed from axial optical aberrations in the acquisition system. An alternating direction method is developed to solve the resulting optimization problem efficiently. According to the structure of the TV regularization and sparse unmixing in the model, the convergence of the alternating direction method can be guaranteed. Experimental results are reported to demonstrate the effectiveness of the TV and sparsity model and the efficiency of the proposed numerical scheme, and the method is compared to the recent Sparse Unmixing via variable Splitting Augmented Lagrangian and TV method by Iordache et al. Numéro de notice : A2013-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227764 En ligne : https://doi.org/10.1109/TGRS.2012.2227764 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32513
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4045 - 4058[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Graph-regularized low-rank representation for destriping of hyperspectral images / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
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Titre : Graph-regularized low-rank representation for destriping of hyperspectral images Type de document : Article/Communication Auteurs : Xiaoqiang Lu, Auteur ; Yulong Wang, Auteur ; Yuan Yuan, Auteur Année de publication : 2013 Article en page(s) : pp 4009 - 4018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] corrélation
[Termes IGN] délignage
[Termes IGN] image hyperspectraleRésumé : (Auteur) Hyperspectral image destriping is a challenging and promising theme in remote sensing. Striping noise is a ubiquitous phenomenon in hyperspectral imagery, which may severely degrade the visual quality. A variety of methods have been proposed to effectively alleviate the effects of the striping noise. However, most of them fail to take full advantage of the high spectral correlation between the observation subimages in distinct bands and consider the local manifold structure of the hyperspectral data space. In order to remedy this drawback, in this paper, a novel graph-regularized low-rank representation (LRR) destriping algorithm is proposed by incorporating the LRR technique. To obtain desired destriping performance, two sides of performing destriping are included: 1) To exploit the high spectral correlation between the observation subimages in distinct bands, the technique of LRR is first utilized for destriping, and 2) to preserve the intrinsic local structure of the original hyperspectral data, the graph regularizer is incorporated in the objective function. The experimental results and quantitative analysis demonstrate that the proposed method can both remove striping noise and achieve cleaner and higher contrast reconstructed results. Numéro de notice : A2013-373 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2226730 En ligne : https://doi.org/10.1109/TGRS.2012.2226730 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32511
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4009 - 4018[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Semisupervised self-learning for hyperspectral image classification / Immaculada Dopido in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
PermalinkSpectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
PermalinkUtility of the wavelet transform for LAI estimation using hyperspectral data / Asim Banskota in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 7 (July 2013)
PermalinkBand grouping versus band clustering in SVM ensemble classification of hyperspectral imagery / Behnaz Bigdeli in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 6 (June 2013)
PermalinkShadow detection in very high spatial resolution aerial images: A comparative study / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)
PermalinkAttraction-repulsion model-based subpixel mapping of multi-/hyperspectral imagery / Xiaohua Tong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)
PermalinkA classification algorithm for hyperspectral images based on synergetics theory / Daniele Cerra in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)
PermalinkCommercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa / Kabir Yunus Peerbhay in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)
PermalinkHistogram curve matching approaches for object-based image classification of land cover and land use / Sory I. Toure in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 5 (May 2013)
PermalinkManifold regularized sparse NMF for hyperspectral unmixing / Xiaqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)
PermalinkModels and methods for automated background density estimation in hyperspectral anomaly detection / Stefania Matteoli in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)
PermalinkPiecewise convex multiple-model endmember detection and spectral unmixing / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)
PermalinkAn experimental comparison of semi-supervised learning algorithms for multispectral image classification / Enmei Tu in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 4 (April 2013)
PermalinkDescription de la campagne aéroportée UMBRA : étude de l'impact anthropique sur les écosystèmes urnbains et naturels avec des images THR multispectrales et hyperspectrales / Karine R.M. Adeline in Revue Française de Photogrammétrie et de Télédétection, n° 202 (Avril 2013)
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PermalinkMultiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 2 (April 2013)
PermalinkObject-based fusion of multitemporal multiangle ENVISAT ASAR and HJ-1B multispectral data for urban land-cover mapping / Yifang Ban in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)
PermalinkSegmentation hyperspectrale de forêts tropicales par arbres de partition binaires / Guillaume Tochon in Revue Française de Photogrammétrie et de Télédétection, n° 202 (Avril 2013)
PermalinkSTARS : A new method for multitemporal remote sensing / Marcio Pupin Mello in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)
PermalinkAssessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study / Gyanesh Chander in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 1 (March 2013)
PermalinkGSICS inter-calibration of infrared channels of geostationary imagers using Metop-IASI / Tim J. Hewison in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 1 (March 2013)
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