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Two heads are better than one / Brian Curtiss in GEO: Geoconnexion international, vol 15 n° 8 (September 2016)
[article]
Titre : Two heads are better than one Type de document : Article/Communication Auteurs : Brian Curtiss, Auteur Année de publication : 2016 Article en page(s) : pp 33 - 37 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement automatique
[Termes IGN] appariement d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] spectroradiométrieRésumé : (éditeur) Using linked spectroradiometers enables far better collection of field reflectance spectra, to improve the matching of remote sensed imagery.Brian Curtiss shows how it can be done Numéro de notice : A2016-662 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81893
in GEO: Geoconnexion international > vol 15 n° 8 (September 2016) . - pp 33 - 37[article]Dirichlet process based active learning and discovery of unknown classes for hyperspectral image classification / Hao Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
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Titre : Dirichlet process based active learning and discovery of unknown classes for hyperspectral image classification Type de document : Article/Communication Auteurs : Hao Wu, Auteur ; Saurabh Prasad, Auteur Année de publication : 2016 Article en page(s) : pp 4882 - 4895 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] classification
[Termes IGN] image hyperspectrale
[Termes IGN] problème de DirichletRésumé : (Auteur) Active learning is an area of significant ongoing research interest for the classification of remotely sensed data, where obtaining efficient training data is both time consuming and expensive. The goal of active learning is to achieve high classification performance by querying as few samples as possible from a large unlabeled data pool. Traditional active learning frameworks all assume the existence of labeled samples for all classes of interest. However, in real-world applications, the unlabeled data pool may contain data from unknown classes that we are not aware of in advance, and a quick detection of them is useful for enriching our training set. In this scenario, traditional active learning methods may not effectively and rapidly detect the unknown classes. We proposed an active learning framework which provides robust classification performance with minimum manual labeling effort while simultaneously discovering unknown (missing) classes. The discovery of unknown classes is particularly suited to an active learning framework where an annotator is in the loop. A Dirichlet process mixture model is utilized in our proposed method to cluster the labeled and unlabeled samples as a whole. If unknown classes exist, they will emerge as new clusters which are different from other existing clusters occupied by known classes, and then, the proposed query strategy will give priority to querying samples in the new clusters. We present experimental results with hyperspectral data to show that our method provides better classification performance compared to existing active learning methods with or without unknown classes. Numéro de notice : A2016-892 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2552507 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2552507 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83072
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4882 - 4895[article]Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing / Paris V. Giampouras in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)
[article]
Titre : Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing Type de document : Article/Communication Auteurs : Paris V. Giampouras, Auteur ; Konstantinos E. Themelis, Auteur ; Athanasios A. Rontogiannis, Auteur ; Konstantinos D. Koutroumbas, Auteur Année de publication : 2016 Article en page(s) : pp 4775 - 4789 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] corrélation automatique de points homologues
[Termes IGN] données clairsemées
[Termes IGN] image hyperspectrale
[Termes IGN] matrice creuseRésumé : (Auteur) In a plethora of applications dealing with inverse problems, e.g., image processing, social networks, compressive sensing, and biological data processing, the signal of interest is known to be structured in several ways at the same time. This premise has recently guided research into the innovative and meaningful idea of imposing multiple constraints on the unknown parameters involved in the problem under study. For instance, when dealing with problems whose unknown parameters form sparse and low-rank matrices, the adoption of suitably combined constraints imposing sparsity and low rankness is expected to yield substantially enhanced estimation results. In this paper, we address the spectral unmixing problem in hyperspectral images. Specifically, two novel unmixing algorithms are introduced in an attempt to exploit both spatial correlation and sparse representation of pixels lying in the homogeneous regions of hyperspectral images. To this end, a novel mixed penalty term is first defined consisting of the sum of the weighted ℓ1 and the weighted nuclear norm of the abundance matrix corresponding to a small area of the image determined by a sliding square window. This penalty term is then used to regularize a conventional quadratic cost function and impose simultaneous sparsity and low rankness on the abundance matrix. The resulting regularized cost function is minimized by: 1) an incremental proximal sparse and low-rank unmixing algorithm; and 2) an algorithm based on the alternating direction method of multipliers. The effectiveness of the proposed algorithms is illustrated in experiments conducted both on simulated and real data. Numéro de notice : A2016-891 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2551327 En ligne : https://doi.org/10.1109/TGRS.2016.2551327 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83071
in IEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4775 - 4789[article]Classifying buildings from point clouds and images / Evangelos Maltezos in GIM international, vol 30 n° 7 (July 2016)
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Titre : Classifying buildings from point clouds and images Type de document : Article/Communication Auteurs : Evangelos Maltezos, Auteur ; Charalabos Ioannnidis, Auteur Année de publication : 2016 Article en page(s) : pp 18 - 21 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] appariement d'images
[Termes IGN] classification dirigée
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] Grèce
[Termes IGN] image optique
[Termes IGN] semis de pointsRésumé : (éditeur) The reconstruction of building outlines provide useful input for land information system. In the city of Kalochory in nethern Greece, a mixed commercial and residential of 33 hectares was selected as a test area to evaluate the classification of buildings. Two data sources were avalaible: airborn Lidar and photographs. These data sources were procesesed to create two separate point clouds.Comparison of the results shows that both data sources can be used for building classification, although more development is needed to improve the robustness of dense image matching. Numéro de notice : A2016-490 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81508
in GIM international > vol 30 n° 7 (July 2016) . - pp 18 - 21[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]Estimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach / Abderrahim Halimi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkFusion of LiDAR orthowaveforms and hyperspectral imagery for shallow river bathymetry and turbidity estimation / Zhigang Pan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkMultiple spectral similarity metrics for surface materials identification using hyperspectral data / Rama Rao Nidamanuri in Geocarto international, vol 31 n° 7 - 8 (July - August 2016)PermalinkRecursive orthogonal projection-based simplex growing algorithm / Hsiao-Chi Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkSparse 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)PermalinkA superresolution land-cover change detection method using remotely sensed images with different spatial resolutions / Xiaodong Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)PermalinkSpectral band selection for urban material classification using hyperspectral libraries / Arnaud Le Bris in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-7 (July 2016)PermalinkFusion of hyperspectral and VHR multispectral image classifications in urban α–areas / Alexandre Hervieu in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol III-3 (July 2016)PermalinkAn interactive tool for semi-automatic feature extraction of hyperspectral data / Zoltan Kovacs in Open geosciences, vol 8 n° 1 (January - July 2016)PermalinkCorrection of atmospheric refraction geolocation error for high resolution optical satellite pushbroom images / Ming Yan in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 6 (June 2016)PermalinkImproving sensor fusion : a parametric method for the geometric coalignment of airborne hyperspectral and lidar data / Maximilian Brell in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkA manifold alignment approach for hyperspectral image visualization with natural color / Danping Liao in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkSupervised classification of very high resolution optical images using wavelet-based textural features / Olivier Regniers in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkThermal infrared reveals vegetation stress / Thomas A. Groen in GIM international, vol 30 n° 6 (June 2016)PermalinkVector attribute profiles for hyperspectral image classification / Erchan Aptoula in IEEE Transactions on geoscience and remote sensing, vol 54 n° 6 (June 2016)PermalinkExploiting joint sparsity for pansharpening : the J-SparseFI algorithm / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkJournées de la recherche IGN 2016 / Anonyme in Géomatique expert, n° 110 (mai - juin 2016)PermalinkKernel-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)PermalinkRemote sensing of alpine glaciers in visible and infrared wavelengths: a survey of advances and prospects / Anshuman Bhardwaj in Geocarto international, vol 31 n° 5 - 6 (May - June 2016)PermalinkRemote sensing technologies for enhancing forest inventories: A review / Joanne C. White in Canadian journal of remote sensing, vol 42 n° 5 ([01/05/2016])Permalink