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vol 80 n° 8 - August 2014 - Research advances in hyperspectral remote sensing (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / American society for photogrammetry and remote sensing
[n° ou bulletin]
est un bulletin de Photogrammetric Engineering & Remote Sensing, PERS / American society for photogrammetry and remote sensing (1975 -)
Titre : vol 80 n° 8 - August 2014 - Research advances in hyperspectral remote sensing Type de document : Périodique Auteurs : American society for photogrammetry and remote sensing, Auteur Année de publication : 2014 Importance : 110 p. Langues : Anglais (eng) Descripteur : [Termes IGN] image hyperspectrale
[Termes IGN] télédétection spatialeNuméro de notice : 105-201408 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=17699 [n° ou bulletin]Contient
- Improved capability in stone pine forest mapping and management in Lebanon using hyperspectral CHTIS-Proba data relative to Landsat ETM+ / Mohamad Awad in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
- Combining hyperspectral and Lidar data for vegetation mapping in the Florida Everglades / Caiyun Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
- Hyperspectral data dimensionality reduction and the impact of multi-seasonal Hyperion EO-1 imagery on classification accuracies of tropical forest species / Manjit Saini in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
- Automated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor / Helge Aasen in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 8 (August 2014)
Spectral identification of materials by reflectance spectral library search / Rama Rao Nidamanuri in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
[article]
Titre : Spectral identification of materials by reflectance spectral library search Type de document : Article/Communication Auteurs : Rama Rao Nidamanuri, Auteur ; A. M. Ramiya, Auteur Année de publication : 2014 Article en page(s) : pp 609-624 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] appariement spectral
[Termes IGN] image hyperspectrale
[Termes IGN] réflectance spectrale
[Termes IGN] signature spectraleRésumé : (auteur) Spectral library search is emerging as a viable approach for material identification and mapping by reusing spectral knowledge gained from hyperspectral remote sensing across space and time. The potential of retrieving meaningful spectral material identifications in the presence of reflectance of spectra of various material types and with various similarity metrics has been assessed in this study. Test reflectance spectra of various vegetation, minerals, soils and urban material types are identified by searching through the composite reflectance spectral library obtained by combining various institutional reflectance spectral libraries. The accuracy of material identifications under various conditions: (i) in the presence of identical, similar and dissimilar spectra; (ii) in the presence of only identical and dissimilar spectra; and (iii) in the presence of only dissimilar spectra has been assessed with several similarity metrics. Results indicate the possibility of obtaining 100% accurate material identifications by library search if the spectral library contains identical spectra. However, the presence of a large number of similar spectra, despite the presence of identical spectra, is found to increase false positives, thereby reducing the accuracy of retrievals to 82% at best. Further, the accuracy of material identifications in the presence of similar spectra is similarity metric-dependent and varied from about 52% (obtained from Binary Encoding) to 82% (obtained from Normalized Spectral Similarity Score). Overall, results support the possibility of using independent reflectance spectral libraries for material identification while calling for robust spectral similarity metrics. Numéro de notice : A2014-418 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.821175 En ligne : https://doi.org/10.1080/10106049.2013.821175 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73953
in Geocarto international > vol 29 n° 5 - 6 (August - October 2014) . - pp 609-624[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2014031 RAB Revue Centre de documentation En réserve L003 Disponible Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing / Jaime Zabalza in ISPRS Journal of photogrammetry and remote sensing, vol 93 (July 2014)
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Titre : Novel Folded-PCA for improved feature extraction and data reduction with hyperspectral imaging and SAR in remote sensing Type de document : Article/Communication Auteurs : Jaime Zabalza, Auteur ; Jinchang Ren, Auteur ; Mingqiang Yang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp. 112 - 122 Langues : Anglais (eng) Descripteur : [Termes IGN] analyse en composantes principales
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] image radar
[Termes IGN] matrice de covarianceRésumé : As a widely used approach for feature extraction and data reduction, Principal Components Analysis (PCA) suffers from high computational cost, large memory requirement and low efficacy in dealing with large dimensional datasets such as Hyperspectral Imaging (HSI). Consequently, a novel Folded-PCA is proposed, where the spectral vector is folded into a matrix to allow the covariance matrix to be determined more efficiently. With this matrix-based representation, both global and local structures are extracted to provide additional information for data classification. Moreover, both the computational cost and the memory requirement have been significantly reduced. Using Support Vector Machine (SVM) for classification on two well-known HSI datasets and one Synthetic Aperture Radar (SAR) dataset in remote sensing, quantitative results are generated for objective evaluations. Comprehensive results have indicated that the proposed Folded-PCA approach not only outperforms the conventional PCA but also the baseline approach where the whole feature sets are used. Numéro de notice : A2014-330 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.04.006 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.04.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73697
in ISPRS Journal of photogrammetry and remote sensing > vol 93 (July 2014) . - pp. 112 - 122[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2014071 RAB Revue Centre de documentation En réserve L003 Disponible Decision fusion in kernel-induced spaces for hyperspectral image classification / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
[article]
Titre : Decision fusion in kernel-induced spaces for hyperspectral image classification Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Saurabh Prasad, Auteur ; James E. Fowler, Auteur Année de publication : 2014 Article en page(s) : pp 3399 - 3411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectraleRésumé : (Auteur) The one-against-one (OAO) strategy is commonly employed with classifiers-such as support vector machines-which inherently provide binary two-class classification in order to handle multiple classes. This OAO strategy is introduced for the classification of hyperspectral imagery using discriminant analysis within kernel-induced feature spaces, producing a pair of algorithms-kernel discriminant analysis and kernel local Fisher discriminant analysis-for dimensionality reduction, which are followed by a quadratic Gaussian maximum-likelihood-estimation classifier. In the proposed approach, a multiclass problem is broken down into all possible binary classifiers, and various decision-fusion rules are considered for merging results from this classifier ensemble. Experimental results using several hyperspectral data sets demonstrate the benefits of the proposed approach-in addition to improved classification performance, the resulting classifier framework requires reduced memory for estimating kernel matrices. Numéro de notice : A2014-309 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2272760 En ligne : https://doi.org/10.1109/TGRS.2013.2272760 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33212
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3399 - 3411[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014061B RAB Revue Centre de documentation En réserve L003 Disponible Feature extraction of hyperspectral images with image fusion and recursive filtering / Xudong Kang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)
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Titre : Feature extraction of hyperspectral images with image fusion and recursive filtering Type de document : Article/Communication Auteurs : Xudong Kang, Auteur ; Shutao Li, Auteur ; Jon Atli Benediktsson, Auteur Année de publication : 2014 Article en page(s) : pp 3742 - 3753 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] filtrage numérique d'image
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectraleRésumé : (Auteur) Feature extraction is known to be an effective way in both reducing computational complexity and increasing accuracy of hyperspectral image classification. In this paper, a simple yet quite powerful feature extraction method based on image fusion and recursive filtering (IFRF) is proposed. First, the hyperspectral image is partitioned into multiple subsets of adjacent hyperspectral bands. Then, the bands in each subset are fused together by averaging, which is one of the simplest image fusion methods. Finally, the fused bands are processed with transform domain recursive filtering to get the resulting features for classification. Experiments are performed on different hyperspectral images, with the support vector machines (SVMs) serving as the classifier. By using the proposed method, the accuracy of the SVM classifier can be improved significantly. Furthermore, compared with other hyperspectral classification methods, the proposed IFRF method shows outstanding performance in terms of classification accuracy and computational efficiency. Numéro de notice : A2014-314 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2275613 En ligne : https://doi.org/10.1109/TGRS.2013.2275613 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33217
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 6 Tome 2 (June 2014) . - pp 3742 - 3753[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014061B RAB Revue Centre de documentation En réserve L003 Disponible Semisupervised dual-geometric subspace projection for dimensionality reduction of hyperspectral image data / Shuyuan Yang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 2 (June 2014)PermalinkSubspace matching pursuit for sparse unmixing of hyperspectral data / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 1 (June 2014)PermalinkDouble constrained NMF for hyperspectral unmixing / Xiaoqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)PermalinkHyperspectral image denoising with a spatial–spectral view fusion strategy / Qiangqiang Yuan in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)PermalinkBayesian context-dependent learning for anomaly classification in hyperspectral imagery / Christopher Ratto in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)PermalinkHyperspectral-based adaptive matched filter detector error as a function of atmospheric water vapor estimation / Allan W. Yarbrough in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)PermalinkImpact of signal contamination on the adaptive detection performance of local hyperspectral anomalies / Stefania Matteoli in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)PermalinkProgressive band selection of spectral unmixing for hyperspectral imagery / Chein-I Chang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 4 (April 2014)PermalinkAbove ground biomass estimation in an African tropical forest with lidar and hyperspectral data / Gaia Vaglio Laurin in ISPRS Journal of photogrammetry and remote sensing, vol 89 (March 2014)PermalinkSemi-automated registration of close-range hyperspectral scans using oriented digital camera imagery and a 3D model / Alessandra A. Sima in Photogrammetric record, vol 29 n° 145 (March - May 2014)Permalink