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A Random Forest class memberships based wrapper band selection criterion : application to hyperspectral / Arnaud Le Bris (2015)
Titre : A Random Forest class memberships based wrapper band selection criterion : application to hyperspectral Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Nicolas Paparoditis , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2015 Conférence : IGARSS 2015, International Geoscience And Remote Sensing Symposium 26/07/2015 31/07/2015 Milan Italie Proceedings IEEE Importance : pp 1112 - 1115 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] carte de confiance
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image hyperspectraleRésumé : (auteur) Hyperspectral imagery generates huge data volumes, consisting of hundreds of contiguous and often highly redundant spectral bands. Difficulties are caused by this high dimensionality. Feature selection (FS) is a possible strategy to reduce the number of bands, consisting in selecting the most relevant bands for a classification problem. It is adapted to the design of superspectral sensor dedicated to specific applications. FS is an optimization problem involving both a metric (that is to say a FS score or criterion measuring the relevance of feature subsets) to optimize and an optimization strategy. In this paper, a wrapper FS score based on Random Forests (RF) and taking into account RF class membership measures was proposed. It was compared to a state-of-the-art wrapper FS score (classification Kappa obtained by RF). Both were then evaluated quantitatively considering both classification performance reached applying different classifiers. An qualitative analysis was also performed to consider the stability/regularity of the selected features along the spectrum. Even though the quantitative evaluation showed little differences between the two tested FS criteria, there seemed to be a trend in favour of the proposed criterion. Taking into account the measures of class membership provided by a RF classifier slightly improved results, regularizing feature selection. Numéro de notice : C2015-022 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2015.7325965 Date de publication en ligne : 12/11/2015 En ligne : http://dx.doi.org/10.1109/IGARSS.2015.7325965 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83168
Titre : Sentinel 2 global reference image Type de document : Article/Communication Auteurs : Cécile Dechoz, Auteur ; Vincent Poulain, Auteur ; Stéphane Massera , Auteur ; Florie Languille, Auteur ; Daniel Greslou, Auteur ; Françoise de Lussy, Auteur ; Céline L'Helguen, Auteur ; C. Picard, Auteur ; Thierry Trémas, Auteur Editeur : Washington : Society of Photo-Optical Instrumentation Engineers SPIE Année de publication : 2015 Collection : SPIE Proceedings num. 9643 Conférence : SPIE 2015, Image and Signal Processing for Remote Sensing XXI 21/09/2015 24/09/2015 Toulouse France Proceedings SPIE Note générale : biblio Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] compensation par faisceaux
[Termes IGN] étalonnage des données
[Termes IGN] image Sentinel-MSI
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] qualité d'imageRésumé : (auteur) Sentinel-2 is a multispectral, high-resolution, optical imaging mission, developed by the European Space Agency (ESA) in the frame of the Copernicus program of the European Commission. In cooperation with ESA, the Centre National d’Etudes Spatiales (CNES) is responsible for the image quality of the project, and will ensure the CAL/VAL commissioning phase. Sentinel-2 mission is devoted the operational monitoring of land and coastal areas, and will provide a continuity of SPOT- and Landsat-type data. Sentinel-2 will also deliver information for emergency services. Launched in 2015 and 2016, there will be a constellation of 2 satellites on a polar sun-synchronous orbit, imaging systematically terrestrial surfaces with a revisit time of 5 days, in 13 spectral bands in visible and shortwave infra-red. Therefore, multi-temporal series of images, taken under the same viewing conditions, will be available. So as to ensure for the multi-temporal registration of the products, specified to be better than 0.3 pixels at 2σ, a Global Reference Image (GRI) will be produced during the CAL/VAL period. This GRI is composed of a set of Sentinel-2 acquisitions, which geometry has been corrected by bundle block adjustment. During L1B processing, Ground Control Points will be taken between this reference image and the sentinel-2 acquisition processed and the geometric model of the image corrected, so as to ensure the good multi-temporal registration. This paper first details the production of the reference during the CALVAL period, and then details the qualification and geolocation performance assessment of the GRI. It finally presents its use in the Level-1 processing chain and gives a first assessment of the multi-temporal registration. Numéro de notice : C2015-059 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1117/12.2195046 Date de publication en ligne : 15/10/2015 En ligne : https://doi.org/10.1117/12.2195046 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90648 Spatial-aware dictionary learning for hyperspectral image classification / Ali Soltani-Farani in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
[article]
Titre : Spatial-aware dictionary learning for hyperspectral image classification Type de document : Article/Communication Auteurs : Ali Soltani-Farani, Auteur ; Hamid R. Rabiee, Auteur ; Seyyed Abbas Hosseini, Auteur Année de publication : 2015 Article en page(s) : pp 527 - 541 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme d'apprentissage
[Termes IGN] classification
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] limite de résolution radiométrique
[Termes IGN] prise en compte du contexte
[Termes IGN] voisinage (relation topologique)Résumé : (Auteur) This paper presents a structured dictionary-based model for hyperspectral data that incorporates both spectral and contextual characteristics of spectral samples. The idea is to partition the pixels of a hyperspectral image into a number of spatial neighborhoods called contextual groups and to model the pixels inside a group as members of a common subspace. That is, each pixel is represented using a linear combination of a few dictionary elements learned from the data, but since pixels inside a contextual group are often made up of the same materials, their linear combinations are constrained to use common elements from the dictionary. To this end, dictionary learning is carried out with a joint sparse regularizer to induce a common sparsity pattern in the sparse coefficients of a contextual group. The sparse coefficients are then used for classification using a linear support vector machine. Experimental results on a number of real hyperspectral images confirm the effectiveness of the proposed representation for hyperspectral image classification. Moreover, experiments with simulated multispectral data show that the proposed model is capable of finding representations that may effectively be used for classification of multispectral resolution samples. Numéro de notice : A2015-037 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2325067 En ligne : https://doi.org/10.1109/TGRS.2014.2325067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75119
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 527 - 541[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015011 RAB Revue Centre de documentation En réserve L003 Disponible Spectral–spatial classification of hyperspectral data via morphological component analysis-based image separation / Zhaohui Xue in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)
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Titre : Spectral–spatial classification of hyperspectral data via morphological component analysis-based image separation Type de document : Article/Communication Auteurs : Zhaohui Xue, Auteur ; Jun Li, Auteur ; Liang Cheng, Auteur ; Peijun Du, Auteur Année de publication : 2015 Article en page(s) : pp 70 - 84 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] classification spectrale
[Termes IGN] image hyperspectraleRésumé : (Auteur) This paper presents a new spectral-spatial classification method for hyperspectral images via morphological component analysis-based image separation rationale in sparse representation. The method consists of three main steps. First, the high-dimensional spectral domain of hyperspectral images is reduced into a low-dimensional feature domain by using minimum noise fraction (MNF). Second, the proposed separation method is acted on each features to generate the morphological components (MCs), i.e., the content and texture components. To this end, the dictionaries for these two components are built by using local curvelet and Gabor wavelet transforms within the randomly chosen image partitions. Then, sparse coding of one of the MCs and update of the associated dictionary are sequentially performed with the other one fixed. To better direct the separation process, an undecimated Haar wavelet with soft threshold is performed for the content component to make it smooth. This process is repeated until some stopping criterion is met. Finally, a support vector machine is adopted to obtain the classification maps based on the MCs. The experimental results with 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 proposed scheme provides better performance when compared with other widely used methods. Numéro de notice : A2015-029 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2318332 En ligne : https://doi.org/10.1109/TGRS.2014.2318332 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75110
in IEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 70 - 84[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2015011 RAB Revue Centre de documentation En réserve L003 Disponible Sub-pixel-scale land cover map updating by integrating change detection and sub-pixel mapping / Xiaodong Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 1 (January 2015)
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Titre : Sub-pixel-scale land cover map updating by integrating change detection and sub-pixel mapping Type de document : Article/Communication Auteurs : Xiaodong Li, Auteur ; Yun Du, Auteur ; Feng Ling, Auteur Année de publication : 2015 Article en page(s) : pp 59 - 67 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse infrapixellaire
[Termes IGN] carte d'occupation du sol
[Termes IGN] détection de changement
[Termes IGN] image à basse résolution
[Termes IGN] implémentation (informatique)
[Termes IGN] mise à jour automatique
[Termes IGN] précision infrapixellaireRésumé : (auteur) Course-resolution remotely sensed images are high in temporal repetition rates, but their low spatial resolution limits their application in updating land cover maps. Our proposed land cover updating method involves the use of coarse-resolution images to update fine-resolution land cover maps. The method comprises change detection and sub-pixel mapping methods. The current coarse-resolution image is unmixed, and the previous fine-resolution map is spatially degraded to produce current and previous class fraction images. A change detection method is applied to these fraction images to create a fine-resolution binary change/non-change map. Finally, a sub-pixel mapping method is applied to update the fine-resolution pixel labels that are changed in the change/ non-change map. The proposed method is compared with a pixel-based classification method and two sub-pixel mapping methods. The proposed method maintains most of the spatial patterns of land cover classes that are unchanged in the previous and current images, whereas other methods cannot. Numéro de notice : A2015-017 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.81.1.59 En ligne : http://www.ingentaconnect.com/content/asprs/pers/2015/00000081/00000001/art00004 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75151
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