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A genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery / Dimitris G. Stavrakoudis in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)
[article]
Titre : A genetic fuzzy-rule-based classifier for land cover classification from hyperspectral imagery Type de document : Article/Communication Auteurs : Dimitris G. Stavrakoudis, Auteur ; G. Galidaki, Auteur ; Ioannis Z. Gitas, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 130 - 148 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par algorithme génétique
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] occupation du solRésumé : (Auteur) This paper proposes the use of a genetic fuzzy-rule-based classification system for land cover classification from hyperspectral images. The proposed classifier, namely, Feature Selective Linguistic Classifier, is constructed through a three-stage learning process. The first stage produces a preliminary fuzzy rule base in an iterative fashion. During this stage, a local feature selection scheme is employed, designed to guide the genetic evolution, through the evaluation of deterministic information about the relevance of each feature with respect to its classification ability. The structure of the model is then simplified in a subsequent postprocessing stage. The performance of the classifier is finally optimized through a genetic tuning stage. An extensive comparative analysis, using an Earth Observing-1 Hyperion satellite image, highlights the quality advantages of the proposed system, when compared with nonfuzzy classifiers, commonly employed in hyperspectral classification tasks. Numéro de notice : A2012-032 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2159613 Date de publication en ligne : 29/07/2011 En ligne : https://doi.org/10.1109/TGRS.2011.2159613 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31480
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 1 (January 2012) . - pp 130 - 148[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012011 RAB Revue Centre de documentation En réserve L003 Disponible Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification / Li Ma in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)
[article]
Titre : Local manifold learning-based k-Nearest-Neighbor for hyperspectral image classification Type de document : Article/Communication Auteurs : Li Ma, Auteur ; Jing Tian, Auteur Année de publication : 2010 Article en page(s) : pp 1099 - 4109 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification barycentrique
[Termes IGN] image AVIRIS
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectraleRésumé : (Auteur) Approaches to combine local manifold learning (LML) and the k -nearest-neighbor (kNN) classifier are investigated for hyperspectral image classification. Based on supervised LML (SLML) and kNN, a new SLML-weighted kNN (SLML-W kNN) classifier is proposed. This method is appealing as it does not require dimensionality reduction and only depends on the weights provided by the kernel function of the specific ML method. Performance of the proposed classifier is compared to that of unsupervised LML (ULML) and SLML for dimensionality reduction in conjunction with the kNN (ULML- kNN and SLML-k NN). Three LML methods, locally linear embedding (LLE), local tangent space alignment (LTSA), and Laplacian eigenmaps, are investigated with these classifiers. In experiments with Hyperion and AVIRIS hyperspectral data, the proposed SLML-WkNN performed better than ULML- kNN and SLML-k NN, and the highest accuracies were obtained using weights provided by supervised LTSA and LLE. Numéro de notice : A2010-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2055876 Date de publication en ligne : 23/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2055876 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30672
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 11 (November 2010) . - pp 1099 - 4109[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2010111 RAB Revue Centre de documentation En réserve L003 Disponible Open source water quality analysis / A. Lo Tauro in GEO: Geoconnexion international, vol 9 n° 8 (september 2010)
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Titre : Open source water quality analysis Type de document : Article/Communication Auteurs : A. Lo Tauro, Auteur Année de publication : 2010 Article en page(s) : pp 24 - 27 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] érosion côtière
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] image MIVIS
[Termes IGN] logiciel libre
[Termes IGN] positionnement par GPS
[Termes IGN] qualité des eaux
[Termes IGN] surveillance du littoral
[Termes IGN] système d'information géographiqueRésumé : (Auteur) A project to monitor, simulate and control water quality parameters has the aim of helping managers and authorities of coastal areas to select the most suitable environmental indicators, taking into account the data available. Hyperspectral analysis of water quality has the potential of enhancing the abilities of resource managers to monitor water bodies in a timely and cost-effective manner. Numéro de notice : A2010-342 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30536
in GEO: Geoconnexion international > vol 9 n° 8 (september 2010) . - pp 24 - 27[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 062-2010081 SL Revue Centre de documentation Revues en salle Disponible Use of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (Smile) in Hyperion Images / A. Dadon in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)
[article]
Titre : Use of derivative calculations and minimum noise fraction transform for detecting and correcting the spectral curvature effect (Smile) in Hyperion Images Type de document : Article/Communication Auteurs : A. Dadon, Auteur ; Eyal Ben-Dor, Auteur ; A. Karnieli, Auteur Année de publication : 2010 Article en page(s) : pp 2603 - 2612 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] filtrage du bruit
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectraleRésumé : (Auteur) Earth Observing-1 Hyperion data were found to be relatively noisy and to contain significant cross-track spectral curvature nonlinearity disturbances, known as the smile/frown effect. A method for the correction of spectral curvature effects (smile) in Hyperion images, termed trend line smile correction (TLSC), is presented. The method is based on the assumption that there is a partial correlation between data spectral nonuniformity, due to the smile and eigenvalues gradient that mostly appears in the first minimum noise fraction (MNF) image (MNF-1). However, MNF-1 consists of both spatial and spectral information. Therefore, it is hypothesized that adaptation applied to MNF-1, according to exclusively spectrally derived parameters (e.g., atmospheric absorption features) can account specifically for the smile effect in the data. A set of normalization factors, calculated from the spectral derivative at the right-hand side of the O2 absorption feature (760 nm), MNF-1 and the moderate-resolution atmospheric transmittance radiative transfer model, are used to scale the initial MNF-1. The image is corrected after the inverse conversion of the MNF to radiance space. The methodology was tested on four different Hyperion scenes and consistently outperformed other tested methods by up to nine times. As a result, thematic mapping, using the TLSC-corrected reflectance data cube, was shown to be consistent with the geology maps of the study area. Numéro de notice : A2010-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2040391 En ligne : https://doi.org/10.1109/TGRS.2010.2040391 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30388
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 6 (June 2010) . - pp 2603 - 2612[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2010061 RAB Revue Centre de documentation En réserve L003 Disponible 065-2010062 RAB Revue Centre de documentation En réserve L003 Disponible A study of trajectory models for satellite image triangulation / Inseong Jeong in Photogrammetric Engineering & Remote Sensing, PERS, vol 76 n° 3 (March 2010)
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Titre : A study of trajectory models for satellite image triangulation Type de document : Article/Communication Auteurs : Inseong Jeong, Auteur ; J. Bethel, Auteur Année de publication : 2010 Article en page(s) : pp 265 - 276 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] image ALOS-PRISM
[Termes IGN] image EO1-Hyperion
[Termes IGN] image EROS
[Termes IGN] image Quickbird
[Termes IGN] image SPOT-HRV
[Termes IGN] image Terra-ASTER
[Termes IGN] implémentation (informatique)
[Termes IGN] orbite
[Termes IGN] point d'appui
[Termes IGN] spatiotriangulationRésumé : (Auteur) Many spaceborne imagery products are provided with metadata or support data having diverse types, representations, frequencies, and conventions. According to the variability of metadata, a compatible physical sensor model approach must be constructed. Among the three component of the sensor model, i.e., trajectory model, projection equations, and parameter subset selection, the construction of the position and attitude trajectory is closely linked with the availability and type of support data. In this paper, we show how trajectory models can be implemented based on support data from six satellite image types: QuickBird, Hyperion, SPOT-3, ASTER. PRISM, and EROS-A. Triangulation for each image is implemented to investigate the feasibility and suitability of the different trajectory models. The results show the effectiveness of some of the simple models while indicating that careful use of dense ephemeris information is necessary. These results are based on having a number of high quality ground control points. Copyright ASPRS Numéro de notice : A2010-088 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.76.3.265 En ligne : https://doi.org/10.14358/PERS.76.3.265 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30284
in Photogrammetric Engineering & Remote Sensing, PERS > vol 76 n° 3 (March 2010) . - pp 265 - 276[article]Merging hyperspectral and panchromatic image data: qualitative and quantitative analysis / M. Cetin in International Journal of Remote Sensing IJRS, vol 30 n° 7 (April 2009)PermalinkNeuro-fuzzy based analysis of hyperspectral imagery / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)PermalinkIntegration of Hyperion satellite data and a household social survey to caracterize the causes and consequences of reforestation patterns in the Northern Ecuadorian Amazon / S.J. Walsh in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 6 (June 2008)PermalinkN-FindR method versus independent component analysis for lithological identification in hyperspectral imagery / C. Gomez in International Journal of Remote Sensing IJRS, vol 28 n°23-24 (December 2007)PermalinkMapping an invasive plant, Phragmites australis [roseau], in coastal wetlands using the EO-1 Hyperion hyperspectral sensor / B.W. Pengra in Remote sensing of environment, vol 108 n° 1 (15/05/2007)PermalinkAn empirical investigation of cross-sensor relationships of NDVI and red/near-infrared reflectance using EO-1 Hyperion data / T. Miura in Remote sensing of environment, vol 100 n° 2 (30 January 2006)PermalinkMapping impervious surface type and sub-pixel abundance using Hyperion hyperspectral imagery / J. Falcone in Geocarto international, vol 20 n° 4 (December 2005 - February 2006)PermalinkA whole image approach using field measurements for transforming EO1 Hyperion hyperspectral data into canopy reflectance spectra / E.W. Ramsey in International Journal of Remote Sensing IJRS, vol 26 n° 8 (April 2005)PermalinkThe development of superspectral approaches for the improvement of land cover classification / M. Gianinetto in IEEE Transactions on geoscience and remote sensing, vol 42 n° 11 (November 2004)PermalinkWavelet transform applied to EO-1 hyperspectral data for forest LAI and crown closure mapping / R. Pu in Remote sensing of environment, vol 91 n° 2 (30/05/2004)PermalinkHyperion, Ikonos, ALI, and ETM+ sensors in the study of African rainforests / Prasad S. Thenkabail in Remote sensing of environment, vol 90 n° 1 (15/03/2004)PermalinkAnalysis of hyperspectral data for estimation of temperate forest canopy nitrogen concentration: Comparison between an Airborne (AVIRIS) and a spaceborne (Hyperion) sensor / M.L. Smith in IEEE Transactions on geoscience and remote sensing, vol 41 n° 6 (June 2003)PermalinkComparative alteration mineral mapping using visible to shortwave infrared (0.4-2.4 um) Hyperion, ALI, and ASTER imagery / B. Hubbard in IEEE Transactions on geoscience and remote sensing, vol 41 n° 6 (June 2003)PermalinkProcessing Hyperion and ALI for forest classification / D.G. Goodenough in IEEE Transactions on geoscience and remote sensing, vol 41 n° 6 (June 2003)Permalink