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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)
[article]
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)
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Titre : Semisupervised self-learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Immaculada Dopido, Auteur ; Jun Li, Auteur ; Prashanth Reddy Marpu, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : pp 4032 - 4044 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] image ROSIS
[Termes IGN] régression logistiqueRésumé : (Auteur) Remotely sensed hyperspectral imaging allows for the detailed analysis of the surface of the Earth using advanced imaging instruments which can produce high-dimensional images with hundreds of spectral bands. Supervised hyperspectral image classification is a difficult task due to the unbalance between the high dimensionality of the data and the limited availability of labeled training samples in real analysis scenarios. While the collection of labeled samples is generally difficult, expensive, and time-consuming, unlabeled samples can be generated in a much easier way. This observation has fostered the idea of adopting semisupervised learning techniques in hyperspectral image classification. The main assumption of such techniques is that the new (unlabeled) training samples can be obtained from a (limited) set of available labeled samples without significant effort/cost. In this paper, we develop a new approach for semisupervised learning which adapts available active learning methods (in which a trained expert actively selects unlabeled samples) to a self-learning framework in which the machine learning algorithm itself selects the most useful and informative unlabeled samples for classification purposes. In this way, the labels of the selected pixels are estimated by the classifier itself, with the advantage that no extra cost is required for labeling the selected pixels using this machine-machine framework when compared with traditional machine-human active learning. The proposed approach is illustrated with two different classifiers: multinomial logistic regression and a probabilistic pixelwise support vector machine. Our experimental results with real hyperspectral images collected by the National Aeronautics and Space Administration Jet Propulsion Laboratory's Airborne Visible-Infrared Imaging Spectrometer and the Reflective Optics Spectrographic Imaging System indicate that the use of self-learning represents an effective and promising strategy in the cont- xt of hyperspectral image classification. Numéro de notice : A2013-374 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2228275 En ligne : https://doi.org/10.1109/TGRS.2012.2228275 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32512
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 4032 - 4044[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Spectral 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)
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Titre : Spectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery Type de document : Article/Communication Auteurs : Yanfeng Gu, Auteur ; Shizhe Wang, Auteur ; Xiuping Jia, Auteur Année de publication : 2013 Article en page(s) : pp 3968 - 3981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espace de Hilbert
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectraleRésumé : (Auteur) In this paper, we address a spectral unmixing problem for hyperspectral images by introducing multiple-kernel learning (MKL) coupled with support vector machines. To effectively solve issues of spectral unmixing, an MKL method is explored to build new boundaries and distances between classes in multiple-kernel Hilbert space (MKHS). Integrating reproducing kernel Hilbert spaces (RKHSs) spanned by a series of different basis kernels in MKHS is able to provide increased power in handling general nonlinear problems than traditional single-kernel learning in RKHS. The proposed method is developed to solve multiclass unmixing problems. To validate the proposed MKL-based algorithm, both synthetic data and real hyperspectral image data were used in our experiments. The experimental results demonstrate that the proposed algorithm has a strong ability to capture interclass spectral differences and improve unmixing accuracy, compared to the state-of-the-art algorithms tested. Numéro de notice : A2013-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227757 En ligne : https://doi.org/10.1109/TGRS.2012.2227757 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32509
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3968 - 3981[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Utility 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)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)Permalink