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Spectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery / L. Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 9 (September 2014)
[article]
Titre : Spectral-angle-based Laplacian Eigenmaps for non linear dimensionality reduction of hyperspectral imagery Type de document : Article/Communication Auteurs : L. Yan, Auteur ; X. Niu, Auteur Année de publication : 2014 Article en page(s) : pp 849 - 861 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] angle d'incidence
[Termes IGN] classification Spectral angle mapper
[Termes IGN] détection de cible
[Termes IGN] distance euclidienne
[Termes IGN] image hyperspectrale
[Termes IGN] réduction
[Termes IGN] réflectance spectrale
[Termes IGN] végétationRésumé : In traditional manifold learning of hyperspectral imagery, distances among pixels are defined in terms of Euclidean distance, which is not necessarilly the best choice because of its sensitivity to variations in spectrum magnitudes. Selecting Laplacian Eignemaps (LE) as the test method, this paper studies the effects of distance metric selection in LE and proposes a spectral-angle-based LE method (LE-SA)to be compared against the traditional LE-based on Euclidean distance (LE-ED). Le-SA and LA-ED were applied to two airborne hyperspectral data sets and the dimensionlity-reduced data were quantitatively evalueted. Experimental results demonstrated that LE-SA is able to suppress the variations within the same type of features, such as variations in vegetation and those in illuminations due to shade orientations, and maintain a higher level of overall separability among different features than LE-ED. Further, the potential usage of a single LA-SA or LE-ED band for target detection is discussed. Numéro de notice : A2014-598 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.9.849 En ligne : https://doi.org/10.14358/PERS.80.9.849 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74888
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 9 (September 2014) . - pp 849 - 861[article]An intelligent approach towards automatic shape modelling and object extraction from satellite images using cellular automata based algorithm / P. V. Arun in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
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Titre : An intelligent approach towards automatic shape modelling and object extraction from satellite images using cellular automata based algorithm Type de document : Article/Communication Auteurs : P. V. Arun, Auteur Année de publication : 2014 Article en page(s) : pp 628-638 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification par réseau neuronal
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image IRS-LISS
[Termes IGN] image Landsat
[Termes IGN] interpolationRésumé : (auteur) Automatic feature extraction domain has witnessed the application of many intelligent methodologies over past decade; however detection accuracy of these approaches were limited as object geometry and contextual knowledge were not given enough consideration. In this paper, we propose a frame work for accurate detection of features along with automatic interpolation, and interpretation by modelling feature shape as well as contextual knowledge using advanced techniques such as SVRF, Cellular Neural Network, Core set, and MACA. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the CNN approach. CNN has been effective in modelling different complex features effectively and complexity of the approach has been considerably reduced using corset optimization. The system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prologue approach. System has been also proved to be effective in providing intelligent interpolation and interpretation of random features. Numéro de notice : A2014-419 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.826738 En ligne : https://doi.org/10.1080/10106049.2013.826738 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73955
in Geocarto international > vol 29 n° 5 - 6 (August - October 2014) . - pp 628-638[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2014031 RAB Revue Centre de documentation En réserve L003 Disponible 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)
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Titre : Automated hyperspectral vegetation index retrieval from multiple correlation matrices with HyperCor Type de document : Article/Communication Auteurs : Helge Aasen, Auteur ; Martin Leon Gnyp, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp. 785 - 795 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] image hyperspectrale
[Termes IGN] indice de végétation
[Termes IGN] logiciel de corrélation
[Termes IGN] matriceRésumé : (Auteur) Hyperspectral vegetation indices have shown high potential for characterizing, classifying, monitoring, and modeling of vegetation and agricultural crops. Correlation matrices from hyperspectral vegetation indices and plant growth parameters help select important wavelength domains and identify redundant bands.
We introduce the software HyperCor for automated pre-processing of narrowband hyperspectral field data and computation of correlation matrices. In addition, we propose a multi-correlation matrix strategy which combines multiple correlation matrices from different datasets and uses more information from each matrix.
We apply this method to a large multi-temporal spectral li-brary to derive vegetation indices and related regression mod-els for rice biomass detection in the tillering, stem elongation, heading and across all growth stages. The models are cali¬brated with data from three consecutive years and validated with two other years. The results reveal that the multi-corre¬lation matrix strategy can improve the model performance by 10 to 62 percent, depending on the growth stage.Numéro de notice : A2014-346 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.8.745 En ligne : https://doi.org/10.14358/PERS.80.8.745 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73719
in Photogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 8 (August 2014) . - pp. 785 - 795[article]A class of cloud detection algorithms based on a MAP-MRF approach in space and time / Gemine Vivone in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
[article]
Titre : A class of cloud detection algorithms based on a MAP-MRF approach in space and time Type de document : Article/Communication Auteurs : Gemine Vivone, Auteur ; Paolo Adesso, Auteur ; Maurizio Longo, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5100 - 5115 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classification
[Termes IGN] corrélation croisée maximale
[Termes IGN] densité de probabilité
[Termes IGN] détection des nuagesRésumé : (Auteur) A recurrent concern in cloud detection approaches is the high misclassification rate for pixels close to cloud edges. We tackle this problem by introducing a novel penalty term within the classical maximum a posteriori probability-Markov random field (MAP-MRF) approach. To improve the classification rate, such term, for which we suggest two different functional forms, accounts for the predictable motion of cloud volumes across images. Two mass tracking techniques are proposed. The first one is an effective and efficient implementation of the probability hypothesis density (PHD) filter, which is based on Gaussian mixtures (GMs) and relies on finite set statistics (FISST). The second one is a region matching procedure based on a maximum cross-correlation (MCC) that is characterized by low computational load. Through extensive tests on simulated images and real data, acquired by the SEVIRI sensor, both methods show a clear performance gain in comparison with classical spatial MRF-based algorithms. Numéro de notice : A2014-435 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2286834 En ligne : https://doi.org/10.1109/TGRS.2013.2286834 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73972
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5100 - 5115[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-2014081B RAB Revue Centre de documentation En réserve L003 Disponible Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products, using Landsat data and C-CAP products / Joseph P. Spruce in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)
[article]
Titre : Geospatial method for computing supplemental multi-decadal US coastal land use and land cover classification products, using Landsat data and C-CAP products Type de document : Article/Communication Auteurs : Joseph P. Spruce, Auteur ; James C. Smoot, Auteur ; Jean T. Ellis, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 470-485 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification ISODATA
[Termes IGN] classification non dirigée
[Termes IGN] détection de changement
[Termes IGN] image Landsat
[Termes IGN] image optique
[Termes IGN] occupation du sol
[Termes IGN] surveillance du littoralRésumé : (auteur) This paper discusses the development and implementation of a method that can be used with multi-decadal Landsat data for computing general coastal US land use and land cover (LULC) maps consisting of seven classes. With Mobile Bay, Alabama as the study region, the method that was applied to derive LULC products for nine dates across a 34-year time span. Classifications were computed and refined using decision rules in conjunction with unsupervised classification of Landsat data and Coastal Change and Analysis Program value-added products. Each classification’s overall accuracy was assessed by comparing stratified random locations to available high spatial resolution satellite and aerial imagery, field survey data and raw Landsat RGBs. Overall classification accuracies ranged from 83 to 91% with overall κ statistics ranging from 0.78 to 0.89. Accurate classifications were computed for all nine dates, yielding effective results regardless of season and Landsat sensor. This classification method provided useful map inputs for computing LULC change products. Numéro de notice : A2014-407 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.798357 Date de publication en ligne : 04/06/2013 En ligne : https://doi.org/10.1080/10106049.2013.798357 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73944
in Geocarto international > vol 29 n° 5 - 6 (August - October 2014) . - pp 470-485[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2014031 RAB Revue Centre de documentation En réserve L003 Disponible 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)PermalinkHyperspectral remote sensing image subpixel target detection based on supervised metric learning / Lefei Zhang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)PermalinkKernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features / Z. He in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)PermalinkA rule-based parameter aided with object-based classification approach for extraction of building and roads from WorldView-2 images / Zahra Ziaei in Geocarto international, vol 29 n° 5 - 6 (August - October 2014)PermalinkApproche de détermination de signature de texture : application à la classification de couverts forestiers d’image satellitaire à haute résolution / Wala Zaaboub in Revue Française de Photogrammétrie et de Télédétection, n° 207 (Juillet 2014)PermalinkAn effective morphological index in automatic recognition of built-up area suitable for high spatial resolution images as ALOS and SPOT data / Bo Yu in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)PermalinkAnnual crop type classification of the US great plains for 2000 to 20011 / Daniel M. Howard in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)PermalinkUne approche basée objet combinée avec les classifieurs avancés (SVM, RF, Extra Trees) pour la détection des changements du bâti / Loubna Elmansouri in Revue internationale de géomatique, vol 24 n° 2 (juin - août 2014)PermalinkAutomatic reconstruction of regular buildings using a shape-based balloon snake model / Diaro Yari in Photogrammetric record, vol 29 n° 146 (June - August 2014)PermalinkCloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model / Qing Cheng in ISPRS Journal of photogrammetry and remote sensing, vol 92 (June 2014)PermalinkDecision 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)PermalinkFeature 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)PermalinkIn search of lost time / Jason Hodgert in GEO: Geoconnexion international, vol 13 n° 6 (june 2014)PermalinkPerformance evaluation of object-based and pixel-based building detection algorithms from very high spatial resolution imagery / Iman Khosravi in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 6 (June 2014)PermalinkSemisupervised 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)PermalinkSignificance analysis of different types of ancillary geodata utilized in a multisource classification process for forest identification in Germany / Michael Förster 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)PermalinkSlow feature analysis for change detection in multispectral imagery / Chen Wu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)Permalink