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Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection / Jie Feng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)
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Titre : Multiple kernel learning based on discriminative kernel clustering for hyperspectral band selection Type de document : Article/Communication Auteurs : Jie Feng, Auteur ; Licheng Jiao, Auteur ; Tao Sun, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 6516 - 6530 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification automatique
[Termes IGN] image hyperspectrale
[Termes IGN] intelligence artificielle
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) In hyperspectral images, band selection plays a crucial role for land-cover classification. Multiple kernel learning (MKL) is a popular feature selection method by selecting the relevant features and classifying the images simultaneously. Unfortunately, a large number of spectral bands in hyperspectral images result in excessive kernels, which limit the application of MKL. To address this problem, a novel MKL method based on discriminative kernel clustering (DKC) is proposed. In the proposed method, a discriminative kernel alignment (KA) (DKA) is defined. Traditional KA measures kernel similarity independently of the current classification task. Compared with KA, DKA measures the similarity of discriminative information by introducing the comparison of intraclass and interclass similarities. It can evaluate both kernel redundancy and kernel synergy for classification. Then, DKA-based affinity-propagation clustering is devised to reduce the kernel scale and retain the kernels having high discrimination and low redundancy for classification. Additionally, an analysis of necessity for DKC in hyperspectral band selection is provided by empirical Rademacher complexity. Experimental results on several hyperspectral images demonstrate the effectiveness of the proposed band selection method in terms of classification performance and computation efficiency. Numéro de notice : A2016-915 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2585961 En ligne : https://doi.org/10.1109/TGRS.2016.2585961 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83140
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 11 (November 2016) . - pp 6516 - 6530[article]SAR image change detection based on correlation kernel and multistage extreme learning machine / Lu Jia in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)
[article]
Titre : SAR image change detection based on correlation kernel and multistage extreme learning machine Type de document : Article/Communication Auteurs : Lu Jia, Auteur ; Ming Li, Auteur ; Peng Zhang, Auteur ; Yan Wu, Auteur Année de publication : 2016 Article en page(s) : pp 5993 - 6006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] appariement d'images
[Termes IGN] apprentissage automatique
[Termes IGN] détection de changement
[Termes IGN] détection de contours
[Termes IGN] image radar moirée
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] séparateur à vaste margeRésumé : (auteur) Designing a kernel function with good discriminating ability and a highly application-adaptive kernelized classifier is the key of many kernel methods. However, not many kernel functions combining directly the bitemporal images' information are designed specifically for change detection tasks. In addition, extreme learning machine (ELM) has not found wide applications in change detection tasks, even though it is a potential kernel method possessing outstanding approximation and generalization capabilities as well as great classification accuracy and efficiency. Therefore, an approach relying on a difference correlation kernel (DCK) and a multistage ELM (MS-ELM) is proposed in this paper for synthetic aperture radar (SAR) image change detection. First, a DCK function is constructed specifically for change detection by measuring the “distance” between any two pixels. The DCK function depicts the cross-time similarities between couples of bitemporal image patches at any cyclic shifts with a kernel correlation operation and the high-order spatial distances between two differently located pixels with an algebraic subtraction. The DCK function possesses strong noise immunity and good identification of changed areas simultaneously. Second, an MS-ELM classifier is constructed for obtaining the change detection result. In MS-ELM, the hidden nodes and weights between the hidden and output layers are updated stage by stage by improving the kernel functions that compose them. Each stage of the MS-ELM is a standard kernel-ELM, and the DCK function is utilized in the first stage. The regenerative kernel functions incorporate the output spatial-neighborhood information of the previous stage for enhancing remarkably the MS-ELM's discriminating ability and noise resistance. The converged result at the last stage of MS-ELM is the final change detection result. Experiments on real SAR image change detection demonstrate the effectiveness of the DCK function and the MS-ELM algorithm, particularly its good identification of changed areas and strong robustness against noise in SAR images. Numéro de notice : A2016-865 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2578438 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2578438 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82901
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 5993 - 6006[article]Efficient multiple-feature learning-based hyperspectral image classification with limited training samples / Chongyue Zhao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
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Titre : Efficient multiple-feature learning-based hyperspectral image classification with limited training samples Type de document : Article/Communication Auteurs : Chongyue Zhao, Auteur ; Xinbo Gao, Auteur ; Ying Wang, Auteur ; Jie Li, Auteur Année de publication : 2016 Article en page(s) : pp 4052 - 4062 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage dirigé
[Termes IGN] classification
[Termes IGN] classification bayesienne
[Termes IGN] extraction
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyauRésumé : (Auteur) Linearly derived features have been widely used in hyperspectral image classification to find linear separability of certain classes in recent years. Moreover, nonlinearly transformed features are more effective for class discrimination in real analysis scenarios. However, few efforts have attempted to combine both linear and nonlinear features in the same framework even if they can demonstrate some complementary properties. Moreover, conventional multiple-feature learning-based approaches deal with different features equally, which is not reasonable. This paper proposes an efficient multiple-feature learning-based model with adaptive weights for effectively classifying complex hyperspectral images with limited training samples. A new diversity kernel function is proposed first to simulate the vision perception and analysis procedure of human beings. It could simultaneously evaluate the contrast differences of global features and spatial coherence. Since existing multiple-kernel feature models are always time-consuming, we then design a new adaptive weighted multiple kernel learning method. It employs kernel projection, which could lower the dimensionalities and also learn kernel weights to further discriminate the classification boundaries. For combining both linear and nonlinear features, this paper also proposes a novel decision fusion strategy. The method combines linear and multiple kernel features to balance the classification results of different classifiers. The proposed scheme is tested on several hyperspectral data sets and extended to multisource feature classification environment. The experimental results show that the proposed classification method outperforms most of the existing ones and significantly reduces the computational complexity. Numéro de notice : A2016-878 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2535538 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2535538 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83041
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 4052 - 4062[article]Sparse and low-rank graph for discriminant analysis of hyperspectral imagery / Wei Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)
[article]
Titre : Sparse and low-rank graph for discriminant analysis of hyperspectral imagery Type de document : Article/Communication Auteurs : Wei Li, Auteur ; Jiabin Liu, Auteur ; Qian Du, Auteur Année de publication : 2016 Article en page(s) : pp 4094 - 4105 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] graphe
[Termes IGN] image hyperspectrale
[Termes IGN] méthode fondée sur le noyau
[Termes IGN] valeur propreRésumé : (Auteur) Recently, sparse graph-based discriminant analysis (SGDA) has been developed for the dimensionality reduction and classification of hyperspectral imagery. In SGDA, a graph is constructed by ℓ1-norm optimization based on available labeled samples. Different from traditional methods (e.g., k-nearest neighbor with Euclidean distance), weights in an ℓ1-graph derived via a sparse representation can automatically select more discriminative neighbors in the feature space. However, the sparsity-based graph represents each sample individually, lacking a global constraint on each specific solution. As a consequence, SGDA may be ineffective in capturing the global structures of data. To overcome this drawback, a sparse and low-rank graph-based discriminant analysis (SLGDA) is proposed. Low-rank representation has been proved to be capable of preserving global data structures, although it may result in a dense graph. In SLGDA, a more informative graph is constructed by combining both sparsity and low rankness to maintain global and local structures simultaneously. Experimental results on several different multiple-class hyperspectral-classification tasks demonstrate that the proposed SLGDA significantly outperforms the state-of-the-art SGDA. Numéro de notice : A2016-879 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2536685 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2536685 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83042
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 4094 - 4105[article]A multilevel point-cluster-based discriminative feature for ALS point cloud classification / Zhenxin Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)
[article]
Titre : A multilevel point-cluster-based discriminative feature for ALS point cloud classification Type de document : Article/Communication Auteurs : Zhenxin Zhang, Auteur ; Liqiang Zhang, Auteur ; Xiaohua Tong, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 3309 - 3321 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification automatique
[Termes IGN] codage
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] semis de points
[Termes IGN] séparateur à vaste marge
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Point cloud classification plays a critical role in point cloud processing and analysis. Accurately classifying objects on the ground in urban environments from airborne laser scanning (ALS) point clouds is a challenge because of their large variety, complex geometries, and visual appearances. In this paper, a novel framework is presented for effectively extracting the shape features of objects from an ALS point cloud, and then, it is used to classify large and small objects in a point cloud. In the framework, the point cloud is split into hierarchical clusters of different sizes based on a natural exponential function threshold. Then, to take advantage of hierarchical point cluster correlations, latent Dirichlet allocation and sparse coding are jointly performed to extract and encode the shape features of the multilevel point clusters. The features at different levels are used to capture information on the shapes of objects of different sizes. This way, robust and discriminative shape features of the objects can be identified, and thus, the precision of the classification is significantly improved, particularly for small objects. Numéro de notice : A2016-851 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2514508 En ligne : https://doi.org/10.1109/TGRS.2016.2514508 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82983
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 6 (June 2016) . - pp 3309 - 3321[article]Kernel-based domain-invariant feature selection in hyperspectral images for transfer learning / Claudio Persello in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkEstimation of forest biomass using multivariate relevance vector regression / Alireza Sharifi in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 1 (January 2016)PermalinkPermalinkPermalinkRegion-kernel-based support vector machines for hyperspectral image classification / Jiangtao Peng in IEEE Transactions on geoscience and remote sensing, vol 53 n° 9 (September 2015)PermalinkSemisupervised transfer component analysis for domain adaptation in remote sensing image classification / Giona Matasci in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)PermalinkSpectral–spatial kernel regularized for hyperspectral image denoising full text / Yuan Yuan in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)PermalinkAnalytical estimation of map readability / Lars Harrie in ISPRS International journal of geo-information, vol 4 n°2 (June 2015)PermalinkPermalinkNoisy data smoothing in DEM construction using least squares support vector machines / C. Chen in Transactions in GIS, vol 18 n° 6 (December 2014)PermalinkDetecting cars in UAV images with a catalog-based approach / Thomas Moranduzzo in IEEE Transactions on geoscience and remote sensing, vol 52 n° 10 tome 1 (October 2014)PermalinkCombining RapidEye and lidar satellite imagery for mapping of mining and mine reclamation / Aaron E. Maxwell in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)PermalinkGeneralized composite kernel framework for hyperspectral image classification / J. Li in IEEE Transactions on geoscience and remote sensing, vol 51 n° 9 (September 2013)PermalinkPermalinkActive learning methods for biophysical parameter estimation / Edoardo Pasolli in IEEE Transactions on geoscience and remote sensing, vol 50 n° 10 Tome 2 (October 2012)PermalinkA multi-resolution hybrid approach for building model reconstruction from lidar data / M. Satari in Photogrammetric record, vol 27 n° 139 (September - November 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)PermalinkParameterizing support vector machines for land cover classification / X. Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 77 n° 1 (January 2011)PermalinkOptimizing Support Vector Machine learning for semi-arid vegetation mapping by using clustering analysis / L. Su in ISPRS Journal of photogrammetry and remote sensing, vol 64 n° 4 (July - August 2009)Permalinkvol 29 n° 21 - October 2008 - Satellite observations of the atmosphere, oceans and their interface in relation to climate, natural hazards and management of coastal zone (Bulletin de International Journal of Remote Sensing IJRS) / G. LevyPermalinkvol 74 n° 10 - October 2008 - Artificial intelligence in remote sensing (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / American society for photogrammetry and remote sensingPermalinkSubpixel urban land cover estimation: comparing cubist, random forests, and support vector regression / J. Walton in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)PermalinkMapping of environmental data using kernel-based methods / Mikhail Kanevski in Revue internationale de géomatique, vol 17 n° 3-4 (septembre 2007 – février 2008)PermalinkA support vector method for anomaly detection in hyperspectral imagery / Amit Banerjee in IEEE Transactions on geoscience and remote sensing, vol 44 n° 8 (August 2006)PermalinkCAp 2006, 8e conférence francophone sur l'apprentissage automatique, 22 - 24 mai 2006, Trégastel, France / Laurent Miclet (2006)Permalink7e conférence francophone sur l'apprentissage automatique, CAp 2005, [Plate-forme AFIA], 30 mai - 3 juin 2005, Nice, France / François Denis (2005)Permalink7es Rencontres des Jeunes Chercheurs en Intelligence Artificielle [Plate-forme AFIA 2005] / Emmanuel Guéré (2005)PermalinkA cost-effective semisupervised classifier approach with kernels / M. Murat Dundar in IEEE Transactions on geoscience and remote sensing, vol 42 n° 1 (January 2004)PermalinkApprentissage automatique / Marc Sebban (1999)PermalinkConférence d'apprentissage 99, actes de CAP'99, Ecole Polytechnique, Palaiseau, 15-18 juin 1999 / Michèle Sebag (1999)Permalink