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Multiple spectral similarity metrics for surface materials identification using hyperspectral data / Rama Rao Nidamanuri in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)
[article]
Titre : Multiple spectral similarity metrics for surface materials identification using hyperspectral data Type de document : Article/Communication Auteurs : Rama Rao Nidamanuri, Auteur Année de publication : 2016 Article en page(s) : pp 845 - 859 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectrale
[Termes IGN] limite de résolution spectrale
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] similitude spectraleRésumé : (Auteur) Modern hyperspectral imaging and non-imaging spectroradiometer has the capability to acquire high-resolution spectral reflectance data required for surface materials identification and mapping. Spectral similarity metrics, due to their mathematical simplicity and insensitiveness to the number of reference labelled spectra, have been increasingly used for material mapping by labelling reflectance spectra in hyperspectral data labelling. For a particular hyperspectral data set, the accuracy of spectral labelling depends considerably upon the degree of unambiguous spectral matching achieved by the spectral similarity metric used. In this work, we propose a new methodology for quantifying spectral similarity for hyperspectral data labelling for surface materials identification. Developed adopting the multiple classifier system architecture, the proposed methodology unifies into a single framework the differential performances of eight different spectral similarity metrics for the quantification of spectral matching for surface materials. The proposed methodology has been implemented on two types of hyperspectral data viz. image (airborne hyperspectral images) and non-image (library spectra) for numerous surface materials identification. Further, the performance of the proposed methodology has been compared with the support vector machines (SVM) approach, and with all the base spectral similarity metrics. The results indicate that, for the hyperspectral images, the performance of the proposed methodology is comparable with that of the SVM. For the library spectra, the proposed methodology shows a consistently higher (increase of about 30% when compared to SVM) classification accuracy. The proposed methodology has the potential to serve as a general library search method for materials identification using hyperspectral data. Numéro de notice : A2016-457 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1086903 Date de publication en ligne : 30/09/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1086903 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81381
in Geocarto international > vol 31 n° 7 - 8 (July - August 2016) . - pp 845 - 859[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016041 RAB Revue Centre de documentation En réserve L003 Disponible Spectral band selection for urban material classification using hyperspectral libraries / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-7 (July 2016)
[article]
Titre : Spectral band selection for urban material classification using hyperspectral libraries Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur ; Xavier Briottet , Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2016 Article en page(s) : pp 33 - 40 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] bande spectrale
[Termes IGN] base de données d'images
[Termes IGN] capteur superspectral
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] milieu urbain
[Termes IGN] optimisation (mathématiques)
[Termes IGN] rayonnement infrarouge
[Termes IGN] signature spectraleRésumé : (auteur) In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000–2400 nm) to material classification was also shown. Numéro de notice : A2016-825 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-III-7-33-2016 Date de publication en ligne : 07/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-annals-III-7-33-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82695
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol III-7 (July 2016) . - pp 33 - 40[article]Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)
[article]
Titre : Fusion of hyperspectral and VHR multispectral image classifications in urban α–areas Type de document : Article/Communication Auteurs : Alexandre Hervieu , Auteur ; Arnaud Le Bris , Auteur ; Clément Mallet , Auteur Année de publication : 2016 Projets : HYEP / Weber, Christiane Article en page(s) : pp 457 - 464 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] méthode de réduction d'énergie
[Termes IGN] occupation du sol
[Termes IGN] optimisation (mathématiques)
[Termes IGN] zone urbaineRésumé : (auteur) An energetical approach is proposed for classification decision fusion in urban areas using multispectral and hyperspectral imagery at distinct spatial resolutions. Hyperspectral data provides a great ability to discriminate land-cover classes while multispectral data,usually at higher spatial resolution, makes possible a more accurate spatial delineation of the classes. Hence, the aim here is to achieve the most accurate classification maps by taking advantage of both data sources at the decision level: spectral properties of the hyperspectral data and the geometrical resolution of multispectral images. More specifically, the proposed method takes into account probability class membership maps in order to improve the classification fusion process. Such probability maps are available using standard classification techniques such as Random Forests or Support Vector Machines. Classification probability maps are integrated into an energy framework where minimization of a given energy leads to better classification maps. The energy is minimized using a graph-cut method called quadratic pseudo-boolean optimization (QPBO) with α-expansion. A first model is proposed that gives satisfactory results in terms of classification results and visual interpretation. This model is compared to a standard Potts models adapted to the considered problem. Finally, the model is enhanced by integrating the spatial contrast observed in the data source of higher spatial resolution (i.e., the multispectral image). Obtained results using the proposed energetical decision fusion process are shown on two urban multispectral/hyperspectral datasets. 2-3% improvement is noticed with respect to a Potts formulation and 3-8% compared to a single hyperspectral-based classification. Numéro de notice : A2016-826 Affiliation des auteurs : LASTIG MATIS (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-III-3-457-2016 Date de publication en ligne : 06/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-annals-III-3-457-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82697
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol III-3 (July 2016) . - pp 457 - 464[article]Documents numériques
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Fusion of hyperspectral and VHR ... - pdf éditeurAdobe Acrobat PDF
Titre : Spatial machine learning applied to multivariate and multimodal images Type de document : Thèse/HDR Auteurs : Gianni Franchi, Auteur ; Jesus Angulo lopez, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2016 Importance : 197 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université de Recherche Paris Sciences et Lettres, préparée à MINES ParisTech, Spécialité : Morphologie MathématiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes principales
[Termes IGN] analyse linéaire des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] krigeage
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] microscope électronique
[Termes IGN] morphologie mathématique
[Termes IGN] régression linéaireIndex. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis focuses on multivariate spatial statistics and machine learning applied to hyperspectral and multimodal and images in remote sensing and scanning electron
microscopy (SEM). In this thesis the following topics are considered:
Fusion of images: SEM allows us to acquire images from a given sample using different modalities. The purpose of these studies is to analyze the interest of fusion of information to improve the multimodal SEM images acquisition. We have modeled
and implemented various techniques of image fusion of information, based in
particular on spatial regression theory. They have been assessed on various
datasets.
Spatial classification of multivariate image pixels: We have proposed a novel approach for pixel classification in multi/hyperspectral images. The aim of this technique is to represent and efficiently describe the spatial/spectral features of multivariate images. These multi-scale deep descriptors aim at representing the content of the image while considering invariances related to the texture and to its geometric transformations.
Spatial dimensionality reduction: We have developed a technique to extract a feature space using morphological principal component analysis. Indeed, in order to take into account the spatial and structural information we used mathematical morphology operatorsNote de contenu : I- Introduction
II- Feature representation and classification for hyperspectral images
III- Fusion of information for multimodal SEM images
IV ConclusionNuméro de notice : 25828 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Morphologie Mathématique : Paris, 2016 nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-01483980v2/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95124 Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)
[article]
Titre : Classification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels Type de document : Article/Communication Auteurs : Leyuan Fang, Auteur ; Shutao Li, Auteur ; Wuhui Duan, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 6663 - 6674 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification spectrale
[Termes IGN] données localisées
[Termes IGN] image hyperspectrale
[Termes IGN] pixelRésumé : (auteur) For the classification of hyperspectral images (HSIs), this paper presents a novel framework to effectively utilize the spectral-spatial information of superpixels via multiple kernels, which is termed as superpixel-based classification via multiple kernels (SC-MK). In the HSI, each superpixel can be regarded as a shape-adaptive region, which consists of a number of spatial neighboring pixels with very similar spectral characteristics. First, the proposed SC-MK method adopts an oversegmentation algorithm to cluster the HSI into many superpixels. Then, three kernels are separately employed for the utilization of the spectral information, as well as spatial information, within and among superpixels. Finally, the three kernels are combined together and incorporated into a support vector machine classifier. Experimental results on three widely used real HSIs indicate that the proposed SC-MK approach outperforms several well-known classification methods. Numéro de notice : A2015-847 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2445767 Date de publication en ligne : 01/07/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2445767 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79197
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 12 (December 2015) . - pp 6663 - 6674[article]Réservation
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Chen in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkOn spectral unmixing resolution using extended support vector machines / Xiaofeng 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)PermalinkTerraSAR-X dual-pol time-series for mapping of wetland vegetation / Julie Betbeder in ISPRS Journal of photogrammetry and remote sensing, vol 107 (September 2015)PermalinkHyperspectral image classification based on three-dimensional scattering wavelet transform / Yuan Yan Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)PermalinkPanorama sur les méthodes de classification des images satellites et techniques d'amélioration de la précision de la classification / O. 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