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Classification of urban tree species using hyperspectral imagery / R. Jensen in Geocarto international, vol 27 n° 5 (August 2012)
[article]
Titre : Classification of urban tree species using hyperspectral imagery Type de document : Article/Communication Auteurs : R. Jensen, Auteur ; P. Hardin, Auteur ; A. Hardin, Auteur Année de publication : 2012 Article en page(s) : pp 443 - 458 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] analyse en composantes principales
[Termes IGN] arbre (flore)
[Termes IGN] arbre urbain
[Termes IGN] espèce végétale
[Termes IGN] flore urbaine
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] image infrarouge
[Termes IGN] indice de végétation
[Termes IGN] Utah (Etas-Unis)Résumé : (Auteur) Urban areas serve as humanity's principal habitat. Because of this, it is important to understand the biophysical components of the urban environment – including the urban forest. The goal of this study was to determine the potential to classify individual urban trees as a function of spectral features derived from airborne hyperspectral data. To determine this, 500 urban trees were identified (through fieldwork) in the built-up zone of Provo-Orem, Utah, USA. Visible and near infrared airborne hyperspectral imagery was collected over the same area. The 500 trees were identified on the images, and spectral features of each tree were extracted. Principal components, vegetation indices, band means, and band ratios were all used as features to discriminate between different tree species. The tree classification was 82% accurate when just the six principal components were used. Classification accuracy increased to 91.4% after combining vegetation indices, band mean values and band ratios. Numéro de notice : A2012-373 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2012.687400 Date de publication en ligne : 24/05/2012 En ligne : https://doi.org/10.1080/10106049.2012.687400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31819
in Geocarto international > vol 27 n° 5 (August 2012) . - pp 443 - 458[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2012051 RAB Revue Centre de documentation En réserve L003 Disponible Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors / R.J. Murphy in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)
[article]
Titre : Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors Type de document : Article/Communication Auteurs : R.J. Murphy, Auteur ; S. Monteiro, Auteur ; S. Schneider, Auteur Année de publication : 2012 Article en page(s) : pp 3066 - 3080 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] Australie occidentale (Australie)
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification Spectral angle mapper
[Termes IGN] image aérienne
[Termes IGN] image hyperspectrale
[Termes IGN] mine
[Termes IGN] ombreRésumé : (Auteur) Hyperspectral data acquired from field-based platforms present new challenges for their analysis, particularly for complex vertical surfaces exposed to large changes in the geometry and intensity of illumination. The use of hyperspectral data to map rock types on a vertical mine face is demonstrated, with a view to providing real-time information for automated mining applications. The performance of two classification techniques, namely, spectral angle mapper (SAM) and support vector machines (SVMs), is compared rigorously using a spectral library acquired under various conditions of illumination. SAM and SVM are then applied to a mine face, and results are compared with geological boundaries mapped in the field. Effects of changing conditions of illumination, including shadow, were investigated by applying SAM and SVM to imagery acquired at different times of the day. As expected, classification of the spectral libraries showed that, on average, SVM gave superior results for SAM, although SAM performed better where spectra were acquired under conditions of shadow. In contrast, when applied to hypserspectral imagery of a mine face, SVM did not perform as well as SAM. Shadow, through its impact upon spectral curve shape and albedo, had a profound impact on classification using SAM and SVM. Numéro de notice : A2012-381 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2178419 Date de publication en ligne : 03/02/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2178419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31827
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 8 (August 2012) . - pp 3066 - 3080[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012081 RAB Revue Centre de documentation En réserve L003 Disponible Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes / J. Im in Geocarto international, vol 27 n° 5 (August 2012)
[article]
Titre : Fusion of feature selection and optimized immune networks for hyperspectral image classification of urban landscapes Type de document : Article/Communication Auteurs : J. Im, Auteur ; Zhong Lu, Auteur ; J. Rhee, Auteur ; R. Jensen, Auteur Année de publication : 2012 Article en page(s) : pp 373 - 393 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme génétique
[Termes IGN] classification par réseau neuronal
[Termes IGN] données lidar
[Termes IGN] entropie
[Termes IGN] image AISA+
[Termes IGN] image EO1-Hyperion
[Termes IGN] image hyperspectrale
[Termes IGN] milieu urbain
[Termes IGN] New York (Etats-Unis ; état)Résumé : (Auteur) The urban landscape is dynamic and complex. As improved remote sensing data in terms of spatial and spectral characteristics became available, more sophisticated methods have been adopted for urban applications. This study proposed and evaluated a classification model incorporating feature selection, artificial immune networks and parameter optimization. Information gain, a broadly applied feature selection metric used in data mining techniques such as decision trees, was used for feature selection. Two types of information gain – binary-class entropy and multiple-class entropy – were investigated. Artificial immune networks have been recently applied to remote sensing classification and have been proven useful especially when multiple parameters of the networks are optimized through a genetic algorithm. The proposed model was tested for urban classification using hyperspectral (i.e. AISA and Hyperion) and LiDAR data over two urban study sites. Results show that the model considerably reduced processing time (70%) for classification without significant accuracy decrease. Numéro de notice : A2012-369 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.642898 Date de publication en ligne : 06/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.642898 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31815
in Geocarto international > vol 27 n° 5 (August 2012) . - pp 373 - 393[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2012051 RAB Revue Centre de documentation En réserve L003 Disponible Hyperspectral band clustering and band selection for urban land cover classification / H. Su in Geocarto international, vol 27 n° 5 (August 2012)
[article]
Titre : Hyperspectral band clustering and band selection for urban land cover classification Type de document : Article/Communication Auteurs : H. Su, Auteur ; Q. Du, Auteur Année de publication : 2012 Article en page(s) : pp 39 - 411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] classification semi-dirigée
[Termes IGN] image hyperspectrale
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] précision de la classification
[Termes IGN] signature spectrale
[Termes IGN] valeur aberranteRésumé : (Auteur) The aim of this study is to combine band clustering with band selection for dimensionality reduction of hyperspectral imagery. The performance of dimensionality reduction is evaluated through urban land cover classification accuracy with the dimensionality-reduced data. Different from unsupervised clustering using all the pixels or supervised clustering requiring labelled pixels, the discussed semi-supervised band clustering needs class spectral signatures only; band selection result is used as initial condition for band clustering; after clustering, a cluster selection step is applied to select clusters to be used in the following data analysis. In this article, we propose to conduct band selection by removing outlier bands in each cluster before finalizing cluster centres. The experimental results in urban land cover classification show that the proposed algorithm can further enhance support vector machine (SVM)-based classification accuracy. Numéro de notice : A2012-370 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2011.643322 Date de publication en ligne : 12/01/2012 En ligne : https://doi.org/10.1080/10106049.2011.643322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31816
in Geocarto international > vol 27 n° 5 (August 2012) . - pp 39 - 411[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2012051 RAB Revue Centre de documentation En réserve L003 Disponible Local coregistration adjustment for anomalous change detection / J. Theiler in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)
[article]
Titre : Local coregistration adjustment for anomalous change detection Type de document : Article/Communication Auteurs : J. Theiler, Auteur ; B. Wohlberg, Auteur Année de publication : 2012 Article en page(s) : pp 3107 - 3116 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection de changement
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] imprécision géométrique
[Termes IGN] scène
[Termes IGN] superposition d'imagesRésumé : (Auteur) We describe an approach for improving the robustness to misregistration of pixel-wise anomalous change detection (ACD) algorithms. The aim of ACD is to distinguish actual anomalous changes from the irrelevant incidental differences that occur throughout the scene. For such change detection to be effective, it is important that corresponding pixels in the two images of interest correspond to the same location in the scene. Indeed, one of the most confounding sources of incidental differences is the inevitable imprecision in the coregistration of the two images. We address this with small local adjustments to the coregistration which leads to a modified misregistration-insensitive measure of anomalousness. Several variants are considered, and the resulting performance improvements are evaluated using both real and simulated changes, and real and simulated misregistration. Numéro de notice : A2012-384 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2179942 Date de publication en ligne : 31/01/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2179942 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31830
in IEEE Transactions on geoscience and remote sensing > vol 50 n° 8 (August 2012) . - pp 3107 - 3116[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2012081 RAB Revue Centre de documentation En réserve L003 Disponible Memory-based cluster sampling for remote sensing image classification / Michele Volpi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)PermalinkRepresentative multiple Kernel learning for classification in hyperspectral imagery / Y. Gu in IEEE Transactions on geoscience and remote sensing, vol 50 n° 7 Tome 2 (July 2012)PermalinkGeometric unmixing of large hyperspectral images: A barycentric coordinate approach / Paul Honeine in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)PermalinkModeling and simulation of polarimetric hyperspectral imaging process / Junping Zhang in IEEE Transactions on geoscience and remote sensing, vol 50 n° 6 (June 2012)PermalinkVariations saisonnière et annuelle de l'indice NDVI en relation avec les herbiers de zosteres (zostera noltii) par images satellites Spot : exemple du Bassin d'Arcachon (France) / J.M. Froidefond in Revue Française de Photogrammétrie et de Télédétection, n° 197 (Juin 2012)PermalinkEstimating urban leaf area index (LAI) of individual trees with hyperspectral data / R. Jensen in Photogrammetric Engineering & Remote Sensing, PERS, vol 78 n° 5 (May 2012)PermalinkView generation for multiview maximum disagreement based active learning for hyperspectral image classification / W. Di in IEEE Transactions on geoscience and remote sensing, vol 50 n° 5 Tome 2 (May 2012)PermalinkClassification of savanna tree species, in the Greater Kruger National Park region, by integrating hyperspectral and LiDAR data in a Random Forest data mining environment / Laven Naidoo in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)PermalinkRobust hyperspectral vision-based classification for multi-season weed mapping / Y. Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 69 (April 2012)PermalinkDevelopment of a network-based method for unmixing of hyperspectral data / V. Karathanassi in IEEE Transactions on geoscience and remote sensing, vol 50 n° 3 (March 2012)PermalinkHyperspectral unmixing based on mixtures of Dirichlet components / J. Nascimento in IEEE Transactions on geoscience and remote sensing, vol 50 n° 3 (March 2012)PermalinkCoupled nonnegative matrix factorization unmixing for hyperspectral and multispectral data fusion / N. Yokoya in IEEE Transactions on geoscience and remote sensing, vol 50 n° 2 (February 2012)PermalinkA 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)PermalinkThe unmixing of atmospheric trace gases from hyperspectral satellite data / P. Addabbo in IEEE Transactions on geoscience and remote sensing, vol 50 n° 1 (January 2012)PermalinkIntercomparison and validation of techniques for spectral unmixing of hyperspectral images : a planetary case study / X. Ceamanos in IEEE Transactions on geoscience and remote sensing, vol 49 n° 11 Tome 1 (November 2011)PermalinkPixel unmixing in hyperspectral data by means of neural networks / Giorgio Licciardi in IEEE Transactions on geoscience and remote sensing, vol 49 n° 11 Tome 1 (November 2011)PermalinkSVM-based unmixing-to-classification conversion for hyperspectral abundance quantification / F. Mianji in IEEE Transactions on geoscience and remote sensing, vol 49 n° 11 Tome 1 (November 2011)PermalinkSimultaneous denoising and intrinsic order selection in hyperspectral imaging / M. Farzam in IEEE Transactions on geoscience and remote sensing, vol 49 n° 9 (September 2011)PermalinkIn situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system / Amr Abd-Elrahman in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)PermalinkIntegration of panoramic hyperspectral imaging with terrestrial lidar data / T. Kurz in Photogrammetric record, vol 26 n° 134 (June - August 2011)PermalinkLocal 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)PermalinkMultiple Spectral–Spatial Classification Approach for Hyperspectral Data / Yuliya Tarabalka in IEEE Transactions on geoscience and remote sensing, vol 48 n° 11 (November 2010)Permalinkvol 48 n° 11 - November 2010 - Special issue on hyperspectral image and signal processing (Bulletin de IEEE Transactions on geoscience and remote sensing) / Geoscience and remote sensing societyPermalinkStatus and future of laser scanning, synthetic aperture radar and hyperspectral remote sensing data for forest biomass assessment / B. Koch in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 6 (November - December 2010)PermalinkOpen source water quality analysis / A. Lo Tauro in GEO: Geoconnexion international, vol 9 n° 8 (september 2010)PermalinkMinimum dispersion constrained nonnegative matrix factorization to unmix hyperspectral data / A. Huck in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)PermalinkSuperresolution enhancement of hyperspectral CHRIS/Proba images with a thin-plate spline nonrigid transform model / J. Chan in IEEE Transactions on geoscience and remote sensing, vol 48 n° 6 (June 2010)PermalinkUse 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)PermalinkMerging hyperspectral and panchromatic image data: qualitative and quantitative analysis / M. Cetin in International Journal of Remote Sensing IJRS, vol 30 n° 7 (April 2009)PermalinkEvaluating AISA+ hyperspectral imagery for mapping black mangrove along the South Texas gulf coast / C. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)PermalinkRemote sensing of soil salinization / G. Metternicht (2009)PermalinkNeuro-fuzzy based analysis of hyperspectral imagery / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)Permalinkvol 46 n° 10 Tome 1 - October 2008 - Special issue on the 2007 International Geoscience and Remote Sensing Symposium (IGARSS'07): sensing and understanding our planet, [actes], Barcelona, July 23-27, 2007. Part 1 of two parts (Bulletin de IEEE Transactions on geoscience and remote sensing) / A. CampsPermalinkvol 46 n° 10 Tome 2 - October 2008 - Special issue on the 2007 International Geoscience and Remote Sensing Symposium (IGARSS'07): sensing and understanding our planet, [actes], Barcelona, July 23-27, 2007. Part 2 of two parts (Bulletin de IEEE Transactions on geoscience and remote sensing) / A. CampsPermalinkPotential accuracy of image orientation of small satellites: a case study of CHRIS/Proba data / Ahmed Shaker in Photogrammetric record, vol 23 n° 123 (September - November 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)PermalinkUnsupervised Image Segmentation based on Texems for Hyperspectral data / Adolfo Martinez-Uso (2008)PermalinkDetermination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data / U. Heiden in Remote sensing of environment, vol 111 n° 4 (28/12/2007)PermalinkApplications de l'imagerie hyperspectrale à l'étude des planètes du système solaire : le cas de Mars et de Titan / S. Le Mouelic in Photo interprétation, vol 43 n° 4 (Décembre 2007)PermalinkBorder vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images / N.G. Kasapoglu in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)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)PermalinkA time-efficient method for anomaly detection in hyperspectral images / O. Duran in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)PermalinkCharacterizing patterns of plant distribution in a southern California salt marsh using remotely sensed topographic and hyperspectral data and local tidal fluctuations / S. Sadro in Remote sensing of environment, vol 110 n° 2 (28/09/2007)PermalinkDerniers développements en télédétection hyperspectrale / V. Carrere in Photo interprétation, vol 43 n° 3 (Septembre 2007)PermalinkFeature extraction of hyperspectral images using wavelet and matching pursuit / Pai-Hui Hsu in ISPRS Journal of photogrammetry and remote sensing, vol 62 n° 2 (June 2007)Permalink