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Spectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery / Yanfeng Gu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 7 Tome 1 (July 2013)
[article]
Titre : Spectral unmixing in multiple-kernel Hilbert space for hyperspectral imagery Type de document : Article/Communication Auteurs : Yanfeng Gu, Auteur ; Shizhe Wang, Auteur ; Xiuping Jia, Auteur Année de publication : 2013 Article en page(s) : pp 3968 - 3981 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] apprentissage automatique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] espace de Hilbert
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectraleRésumé : (Auteur) In this paper, we address a spectral unmixing problem for hyperspectral images by introducing multiple-kernel learning (MKL) coupled with support vector machines. To effectively solve issues of spectral unmixing, an MKL method is explored to build new boundaries and distances between classes in multiple-kernel Hilbert space (MKHS). Integrating reproducing kernel Hilbert spaces (RKHSs) spanned by a series of different basis kernels in MKHS is able to provide increased power in handling general nonlinear problems than traditional single-kernel learning in RKHS. The proposed method is developed to solve multiclass unmixing problems. To validate the proposed MKL-based algorithm, both synthetic data and real hyperspectral image data were used in our experiments. The experimental results demonstrate that the proposed algorithm has a strong ability to capture interclass spectral differences and improve unmixing accuracy, compared to the state-of-the-art algorithms tested. Numéro de notice : A2013-371 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2227757 En ligne : https://doi.org/10.1109/TGRS.2012.2227757 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32509
in IEEE Transactions on geoscience and remote sensing > vol 51 n° 7 Tome 1 (July 2013) . - pp 3968 - 3981[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2013071A RAB Revue Centre de documentation En réserve L003 Disponible Utility of the wavelet transform for LAI estimation using hyperspectral data / Asim Banskota in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 7 (July 2013)
[article]
Titre : Utility of the wavelet transform for LAI estimation using hyperspectral data Type de document : Article/Communication Auteurs : Asim Banskota, Auteur ; Randolph H. Wynne, Auteur ; Shawn P. Serbin, Auteur ; et al., Auteur Année de publication : 2013 Article en page(s) : 662 p. ; pp 653 - 662 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme génétique
[Termes IGN] forêt tempérée
[Termes IGN] image AVIRIS
[Termes IGN] image hyperspectrale
[Termes IGN] Leaf Area Index
[Termes IGN] transformation en ondelettes
[Termes IGN] Wisconsin (Etats-Unis)Résumé : (Auteur) We employed the discrete wavelet transform to reflectance spectra obtained from hyperspectral data to improve estimation of LAi in temperate forests. We estimated LAl for 32 plots across a range afforest types in Wisconsin using hemispherical photography. Plot spectra were extracted from AVIRIS data and transformed into wavelet features using the Haar wavelet. Separately, subsets of spectral bands and the Haar features selected by a genetic algorithm were used as independent variables in linear regressions. Models using wavelet coefficients explained the most variance for both broadleaf plots (R2 = 0.90 for wavelet features versus R2 = 0.80 for spectral bands) and all plots independent afforest type (R2 = 0.79 for wavelet features vs. R2 = 0.58 for spectral bands). The forest-type specific models were better than the models using all plots combined. Overall, wavelet features appear superior to band reflectances alone for estimating temperate forest LAI using hyperspectral data. Numéro de notice : A2013-394 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.14358/PERS.79.7.653 En ligne : https://doi.org/10.14358/PERS.79.7.653 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32532
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 7 (July 2013) . - 662 p. ; pp 653 - 662[article]Band grouping versus band clustering in SVM ensemble classification of hyperspectral imagery / Behnaz Bigdeli in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 6 (June 2013)
[article]
Titre : Band grouping versus band clustering in SVM ensemble classification of hyperspectral imagery Type de document : Article/Communication Auteurs : Behnaz Bigdeli, Auteur ; Farhad Samadzadegan, Auteur ; Peter Reinartz, Auteur Année de publication : 2013 Article en page(s) : pp 523 - 533 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] image hyperspectrale
[Termes IGN] regroupement de donnéesRésumé : (Auteur) Due to the dense sampling of spectral signatures of land covers, hyperspectral images have a better discrimination among similar ground cover classes than traditional remote sensing data. However, these images are usually composed of tens or hundreds of spectrally close bands, which result in high redundancy and great amount of computation time in hyperspectral image classification. In addition, the large number of spectral bands, but limited availability of training samples creates the problem of Hughes phenomenon. Consequently, traditional classification strategies have often limited performance in classification of hyperspectral imagery. Referring to the limitation of single classifiers in these situations, classifier ensemble system may exhibit better performance. This paper presents a method for classification of hyperspectral data based on two concepts of Band Clustering (BC) and Band Grouping [eg] through a Support Vector machine (SVM) ensemble system. The proposed method uses the BC\BG strategies to split data into few band portions. After this step, we applied SVM on each band cluster\group that is produced in previous step. Finally, Naive Bayes as a classifier fusion method combines the decisions of SVM classifiers. Experimental results show that the proposed method improves the classification accuracy in comparison to the standard SVM and to feature selection methods. Numéro de notice : A2013-362 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.6.523 En ligne : https://doi.org/10.14358/PERS.79.6.523 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32500
in Photogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 6 (June 2013) . - pp 523 - 533[article]Shadow detection in very high spatial resolution aerial images: A comparative study / Karine R.M. Adeline in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)
[article]
Titre : Shadow detection in very high spatial resolution aerial images: A comparative study Type de document : Article/Communication Auteurs : Karine R.M. Adeline, Auteur ; M. Chen, Auteur ; Xavier Briottet , Auteur ; S.K. Pan, Auteur ; Nicolas Paparoditis , Auteur Année de publication : 2013 Article en page(s) : pp 21 - 38 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] canyon urbain
[Termes IGN] détection d'ombre
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] rayonnement lumineux
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] seuillage d'image
[Termes IGN] simulation numérique
[Termes IGN] test de performanceRésumé : (Auteur) Automatic shadow detection is a very important pre-processing step for many remote sensing applications, particularly for images acquired with high spatial resolution. In complex urban environments, shadows may occupy a significant portion of the image. Ignoring these regions would lead to errors in various applications, such as atmospheric correction and classification. To better understand the radiative impact of shadows, a physical study was conducted through the simulation of a synthetic urban canyon scene. Its results helped to explain the most common assumptions made on shadows from a physical point of view in the literature. With this understanding, state-of-the-art methods on shadow detection were surveyed and categorized into six classes: histogram thresholding, invariant color models, object segmentation, geometrical methods, physics-based methods, unsupervised and supervised machine learning methods. Among them, some methods were selected and tested on a large dataset of multispectral and hyperspectral airborne images with high spatial resolution. The dataset chosen contains a large variety of typical occidental urban scenes. The results were compared based on accurate reference shadow masks. In these experiments, histogram thresholding on RGB and NIR channels performed the best with an average accuracy of 92.5%, followed by physics-based methods, such as Richter’s method with 90.0%. Finally, this paper analyzes and discusses the limits of these algorithms, concluding with some recommendations for shadow detection. Numéro de notice : A2013-296 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.02.003 Date de publication en ligne : 03/04/2013 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2013.02.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32434
in ISPRS Journal of photogrammetry and remote sensing > vol 80 (June 2013) . - pp 21 - 38[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2013061 RAB Revue Centre de documentation En réserve L003 Disponible Texture augmented detection of macrophyte species using decision trees / Cameron Proctor in ISPRS Journal of photogrammetry and remote sensing, vol 80 (June 2013)
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Titre : Texture augmented detection of macrophyte species using decision trees Type de document : Article/Communication Auteurs : Cameron Proctor, Auteur ; Yuhong He, Auteur ; Vincent Robinson, Auteur Année de publication : 2013 Article en page(s) : pp 10 - 20 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algue
[Termes IGN] classification par arbre de décision
[Termes IGN] image panchromatique
[Termes IGN] image Quickbird
[Termes IGN] macrophyte
[Termes IGN] précision de la classification
[Termes IGN] rivière
[Termes IGN] séparabilité
[Termes IGN] texture d'imageRésumé : (Auteur) Image classification using multispectral sensors has shown good performance in detecting macrophytes at the species level. However, species level classification often does not utilize the texture information provided by high resolution images. This study investigated whether image texture provides useful vector(s) for the discrimination of monospecific stands of three floating macrophyte species in Quickbird imagery of the South Nation River. Semivariograms indicated that window sizes of 5 x 5 and 13 x 13 pixels were the most appropriate spatial scales for calculation of the grey level co-occurrence matrix and subsequent texture attributes from the multispectral and panchromatic bands. Of the 214 investigated vectors (13 Haralick texture attributes * 15 bands + 9 spectral bands + 10 transformations/indices), feature selection determined which combination of spectral and textural vectors had the greatest class separability based on the Mann–Whitney U-test and Jefferies–Matusita distance. While multispectral red and near infrared (NIR) performed satisfactorily, the addition of panchromatic-dissimilarity slightly improved class separability and the accuracy of a decision tree classifier (Kappa: red/NIR/panchromatic-dissimilarity – 93.2% versus red/NIR – 90.4%). Class separability improved by incorporating a second texture attribute, but resulted in a decrease in classification accuracy. The results suggest that incorporating image texture may be beneficial for separating stands with high spatial heterogeneity. However, the benefits may be limited and must be weighed against the increased complexity of the classifier. Numéro de notice : A2013-295 Affiliation des auteurs : non IGN Thématique : BIODIVERSITE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2013.02.022 En ligne : https://doi.org/10.1016/j.isprsjprs.2013.02.022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32433
in ISPRS Journal of photogrammetry and remote sensing > vol 80 (June 2013) . - pp 10 - 20[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 081-2013061 RAB Revue Centre de documentation En réserve L003 Disponible Use of handheld thermal imager data for airborne mapping of fire radiative power and energy and flame front rate of spread / Ronan Paugam in IEEE Transactions on geoscience and remote sensing, vol 51 n° 6 Tome 1 (June 2013)PermalinkUsing reverse viewshed analysis to assess the location correctness of visually generated VGI / Hansi Senaratne in Transactions in GIS, vol 17 n° 3 (June 2013)PermalinkAttraction-repulsion model-based subpixel mapping of multi-/hyperspectral imagery / Xiaohua Tong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkChange detection and deformation analysis in point clouds: Application to rock face monitoring / Marco Scaioni in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 5 (May 2013)PermalinkA classification algorithm for hyperspectral images based on synergetics theory / Daniele Cerra in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkCommercial tree species discrimination using airborne AISA Eagle hyperspectral imagery and partial least squares discriminant analysis (PLS-DA) in KwaZulu–Natal, South Africa / Kabir Yunus Peerbhay in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)PermalinkHistogram curve matching approaches for object-based image classification of land cover and land use / Sory I. Toure in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 5 (May 2013)PermalinkManifold regularized sparse NMF for hyperspectral unmixing / Xiaqiang Lu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkModels and methods for automated background density estimation in hyperspectral anomaly detection / Stefania Matteoli in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkPiecewise convex multiple-model endmember detection and spectral unmixing / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkRegion-based automatic building and forest change detection on Cartosat-1 stereo imagery / Jing Tian in ISPRS Journal of photogrammetry and remote sensing, vol 79 (May 2013)PermalinkA sparse image fusion algorithm with application to pan-sharpening / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)PermalinkAn experimental comparison of semi-supervised learning algorithms for multispectral image classification / Enmei Tu in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 4 (April 2013)PermalinkDécision cumulative pour la vision dynamique des systèmes / Samia Bouchafa in Revue Française de Photogrammétrie et de Télédétection, n° 202 (Avril 2013)PermalinkDescription de la campagne aéroportée UMBRA : étude de l'impact anthropique sur les écosystèmes urnbains et naturels avec des images THR multispectrales et hyperspectrales / Karine R.M. Adeline in Revue Française de Photogrammétrie et de Télédétection, n° 202 (Avril 2013)PermalinkLow altitude aerial photography applications for digital surface models creation in archaeology / José-Angel Martinez-Del-Pozo in Transactions in GIS, vol 17 n° 2 (April 2013)PermalinkMultiple-spectral-band CRFs for denoising junk bands of hyperspectral imagery / Ping Zhong in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 2 (April 2013)PermalinkObject-based fusion of multitemporal multiangle ENVISAT ASAR and HJ-1B multispectral data for urban land-cover mapping / Yifang Ban in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)PermalinkRational function model in processing historical aerial photographs / Ruijin Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 4 (April 2013)PermalinkSegmentation hyperspectrale de forêts tropicales par arbres de partition binaires / Guillaume Tochon in Revue Française de Photogrammétrie et de Télédétection, n° 202 (Avril 2013)PermalinkSTARS : A new method for multitemporal remote sensing / Marcio Pupin Mello in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)PermalinkThe emergency social network: Geovisualising Twitter provides early warnings and on-scene reports to first responders and emergency managers / Deborah Davis in GEO: Geoconnexion international, vol 12 n° 4 (april 2013)PermalinkAssessment of spectral, misregistration, and spatial uncertainties inherent in the cross-calibration study / Gyanesh Chander in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 1 (March 2013)PermalinkGSICS inter-calibration of infrared channels of geostationary imagers using Metop-IASI / Tim J. Hewison in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 1 (March 2013)PermalinkLearning with transductive SVM for semisupervised pixel classification of remote sensing imagery / Ujjwal Maulik in ISPRS Journal of photogrammetry and remote sensing, vol 77 (March 2013)PermalinkSampling piecewise convex unmixing and endmember extraction / Alina Zare in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 2 (March 2013)PermalinkTopological gradient connection analysis for feature detection / Chao-Yuan Lo in Photogrammetric record, vol 28 n° 141 (March - May 2013)PermalinkClassification and reconstruction from random projections for hyperspectral imagery / W. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkA graph-based classification method for hyperspectral images / J. Bai in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkRetrieval of effective leaf area index in heterogeneous forests with terrestrial laser scanning / G. Zheng in IEEE Transactions on geoscience and remote sensing, vol 51 n° 2 (February 2013)PermalinkSpectral angle mapper and object-based classification combined with hyperspectral remote sensing imagery for obtaining land use/cover mapping in a Mediterranean region / George P. Petropoulos in Geocarto international, vol 28 n° 1-2 (February - May 2013)PermalinkSpectral material mapping using hyperspectral imagery : a review of spectral matching and library search methods / Sennaraj Vishnu in Geocarto international, vol 28 n° 1-2 (February - May 2013)PermalinkAppariement entre images de point de vue éloignés par utilisation de carte de profondeur / Narut Soontranon (2013)PermalinkComparaison et évaluation de méthodes d'extraction automatique d'objets sur des images optique et radar / Charlotte Benedetto (2013)PermalinkComparison of VHR panchromatic texture features for tillage mapping / Nesrine Chehata (juillet 2013)PermalinkContribution of texture and red-edge band for vegetated areas detection and identification / Arnaud Le Bris (2013)PermalinkCrop yield estimation based on unsupervised linear unmixing of multidate hyperspectral imagery / B. Luo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 1 (January 2013)PermalinkEuroSDR project Commission 1, Radiometric aspects of digital photogrammetric images / Eija Honkavaara (2013)PermalinkPermalinkA hybrid multiview stereo algorithm for modeling urban scenes / Florent Lafarge in IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI, vol 35 n° 1 (January 2013)PermalinkMapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430–970 nm) / R. Murphy in ISPRS Journal of photogrammetry and remote sensing, vol 75 (January 2013)PermalinkMaterial reflectance retrieval in urban tree shadows with physics-based empirical atmospheric correction / Karine R.M. Adeline (2013)PermalinkA new technique using infrared satellite measurements to improve the accuracy of the CALIPSO cloud-aerosol discrimination method / A. Naeger in IEEE Transactions on geoscience and remote sensing, vol 51 n° 1 Tome 2 (January 2013)PermalinkPermalinkPredicting surface fuel models and fuel metrics using Lidar and CIR imagery in a dense, mountainous forest / Marek Jakubowksi in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 1 (January 2013)Permalink