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A batch-mode regularized multimetric active learning framework for classification of hyperspectral images / Zhou Zhang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 11 (November 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 11 (November 2017) . - pp 6594 - 6609 Titre : A batch-mode regularized multimetric active learning framework for classification of hyperspectral images Type de document : Article/Communication Auteurs : Zhou Zhang, Auteur ; Melba M. Crawford, Auteur Année de publication : 2017 Article en page(s) : pp 6594 - 6609 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] image hyperspectrale [Termes IGN] précision de la classification Résumé : (Auteur) Techniques that combine multiple types of features, such as spectral and spatial features, for hyperspectral image classification can often significantly improve the classification accuracy and produce a more reliable thematic map. However, the high dimensionality of the input data and the typically limited quantity of labeled samples are two key challenges that affect classification performance of supervised methods. In order to simultaneously deal with these issues, a regularized multimetric active learning (AL) framework is proposed which consists of three main parts. First, a regularized multimetric learning approach is proposed to jointly learn distinct metrics for different types of features. The regularizer incorporates the unlabeled data based on the neighborhood relationship, which helps avoid overfitting at early stages of AL, when the quantity of training data is particularly small. Then, as AL proceeds, the regularizer is also updated through similarity propagation, thus taking advantage of informative labeled samples. Finally, multiple features are projected into a common feature space, in which a new batch-mode AL strategy combining uncertainty and diversity is utilized in conjunction with k-nearest neighbor classification to enrich the set of labeled samples. In order to evaluate the effectiveness of the proposed framework, the experiments were conducted on two benchmark hyperspectral data sets, and the results were compared to those achieved by several other state-of-the-art AL methods. Numéro de notice : A2017-760 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2730583 En ligne : https://doi.org/10.1109/TGRS.2017.2730583 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88788 [article]

Active-metric learning for classification of remotely sensed hyperspectral images / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 4 (April 2016) . - pp 1925 - 1939 Titre : Active-metric learning for classification of remotely sensed hyperspectral images Type de document : Article/Communication Auteurs : Edoardo Pasolli, Auteur ; Hsiuhan Lexie Yang, Auteur ; Melba M. Crawford, Auteur Année de publication : 2016 Article en page(s) : pp 1925 - 1939 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] classification barycentrique [Termes IGN] classification dirigée [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques Résumé : (Auteur) Classification of remotely sensed hyperspectral images via supervised approaches is typically affected by high dimensionality of the spectral data and a limited number of labeled samples. Dimensionality reduction via feature extraction and active learning (AL) are two approaches that researchers have investigated independently to deal with these two problems. In this paper, we propose a new method in which the feature extraction and AL steps are combined into a unique framework. The idea is to learn and update a reduced feature space in a supervised way at each iteration of the AL process, thus taking advantage of the increasing labeled information provided by the user. In particular, the computation of the reduced feature space is based on the large-margin nearest neighbor (LMNN) metric learning principle. This strategy is applied in conjunction with k-nearest neighbor ( k-NN) classification, for which a new sample selection strategy is proposed. The methodology is validated experimentally on four benchmark hyperspectral data sets. Good improvements in terms of classification accuracy and computational time are achieved with respect to the state-of-the-art strategies that do not combine feature extraction and AL. Numéro de notice : A2016-836 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2490482 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2490482 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82880 [article]

Point-to-plane registration of terrestrial laser scans / D. Grant in ISPRS Journal of photogrammetry and remote sensing, vol 72 (August 2012)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 72 (August 2012) . - pp 16 - 26 Titre : Point-to-plane registration of terrestrial laser scans Type de document : Article/Communication Auteurs : D. Grant, Auteur ; J. Bethel, Auteur ; Melba M. Crawford, Auteur Année de publication : 2012 Article en page(s) : pp 16 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] compensation par moindres carrés [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] point d'appui [Termes IGN] superposition de données [Termes IGN] télémétrie laser terrestre Résumé : (Auteur) The registration of pairs of Terrestrial Laser Scanning data (TLS) is an integral precursor to 3D data analysis. Of specific interest in this research work is the class of approaches that is considered to be fine registration and which does not require any targets or tie points. This paper presents a pairwise fine registration approach called P2P that is formulated using the General Least Squares adjustment model. Given some initial registration parameters, the proposed P2P approach utilizes the scanned points and estimated planar features of both scans, along with their stochastic properties. These quantities are used to determine the optimum registration parameters in the least squares sense. The proposed P2P approach was tested on both simulated and real TLS data, and experimental results showed it to be four times more accurate than the registration approach of Chen and Medioni (1991). Numéro de notice : A2012-493 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.05.007 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.05.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31939 [article]

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081-2012061	SL	Revue	Centre de documentation	Revues en salle	Disponible

View 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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 5 Tome 2 (May 2012) . - pp 1942 - 1954 Titre : View generation for multiview maximum disagreement based active learning for hyperspectral image classification Type de document : Article/Communication Auteurs : W. Di, Auteur ; Melba M. Crawford, Auteur Année de publication : 2012 Article en page(s) : pp 1942 - 1954 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage automatique [Termes IGN] apprentissage dirigé [Termes IGN] classification dirigée [Termes IGN] image AVIRIS [Termes IGN] image EO1-Hyperion [Termes IGN] image hyperspectrale [Termes IGN] regroupement de points Résumé : (Auteur) Active learning (AL) seeks to interactively construct a smaller training data set that is the most informative and useful for the supervised classification task. Based on the multiview Adaptive Maximum Disagreement AL method, this study investigates the principles and capability of several approaches for the view generation for hyperspectral data classification, including clustering, random selection, and uniform subset slicing methods, which are then incorporated with dynamic view updating and feature space bagging strategies. Tests on Airborne Visible/Infrared Imaging Spectrometer and Hyperion hyperspectral data sets show excellent performance as compared with random sampling and the simple version support vector machine margin sampling, a state-of-the-art AL method. Numéro de notice : A2012-189 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2168566 En ligne : https://doi.org/10.1109/TGRS.2011.2168566 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31636 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2012051B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Exploiting class hierarchies for knowledge transfer in hyperspectral data / S. Rajan in IEEE Transactions on geoscience and remote sensing, vol 44 n° 11 Tome 2 (November 2006)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 44 n° 11 Tome 2 (November 2006) . - pp 3408 - 3417 Titre : Exploiting class hierarchies for knowledge transfer in hyperspectral data Type de document : Article/Communication Auteurs : S. Rajan, Auteur ; J. Ghosh, Auteur ; Melba M. Crawford, Auteur Année de publication : 2006 Article en page(s) : pp 3408 - 3417 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification ascendante hiérarchique [Termes IGN] classification non dirigée [Termes IGN] données multitemporelles [Termes IGN] image hyperspectrale [Termes IGN] signature spectrale Résumé : (Auteur) Obtaining ground truth for classification of remotely sensed data is time consuming and expensive, resulting in poorly represented signatures over large areas. In addition, the spectral signatures of a given class vary with location and/or time. Therefore, successful adaptation of a classifier designed from the available labeled data to classify new hyperspectral images acquired over other geographic locations or subsequent times is difficult, if minimal additional labeled data are available. In this paper, the binary hierarchical classifier is used to propose a knowledge transfer framework that leverages the information extracted from the existing labeled data to classify spatially separate and multitemporal test data. Experimental results show that in the absence of any labeled data in the new area, the approach is better than a direct application of the original classifier on the new data. Moreover, when small amounts of the labeled data are available from the new area, the framework offers further improvements through semisupervised learning mechanisms and compares favorably with previously proposed methods. Copyright IEEE Numéro de notice : A2006-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.878442 En ligne : https://doi.org/10.1109/TGRS.2006.878442 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28251 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-06111B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Result from EO-1 experiment: a comparative study of earth observing-1 Advanced Land Imager (ALI) and Landsat ETM+ data for land cover mapping in the Okavango delta, Botswana / A.L. Neunschwander in International Journal of Remote Sensing IJRS, vol 26 n° 19 (October 2005)  

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Fusing interferometric radar and Laser altimeter data to estimate surface topography and vegetation heights / K.C. Slatton in IEEE Transactions on geoscience and remote sensing, vol 39 n° 11 (November 2001)  

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Best-bases feature extraction algorithms for classification of hyperspectral data / Satish Kumar in IEEE Transactions on geoscience and remote sensing, vol 39 n° 7 (July 2001)  

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