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A novel semisupervised active-learning algorithm for hyperspectral image classification / Zengmao Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)
[article]
Titre : A novel semisupervised active-learning algorithm for hyperspectral image classification Type de document : Article/Communication Auteurs : Zengmao Wang, Auteur ; Bo Du, Auteur ; Lefei Zhang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 3071 - 3083 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse de groupement
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage semi-dirigé
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) Less training samples are a challenging problem in hyperspectral image classification. Active learning and semisupervised learning are two promising techniques to address the problem. Active learning solves the problem by improving the quality of the training samples, while semisupervised learning solves the problem by increasing the quantity of the training samples. However, they pay too much attention to the discriminative information in the unlabeled data, leading to information bias to train supervised models, and much more effort to label samples. Therefore, a method to discover representativeness and discriminativeness by semisupervised active learning is proposed. It takes advantages of both active learning and semisupervised learning. The representativeness and discriminativeness are discovered with a labeling process based on a supervised clustering technique and classification results. Specifically, the supervised clustering results can discover important structural information in the unlabeled data, and the classification results are also highly confidential in the active-learning process. With these clustering results and classification results, we can assign pseudolabels to the unlabeled data. Meanwhile, the unlabeled samples that cannot be assigned with pseudolabels with high confidence at each iteration are regarded as candidates in active learning. The methodology is validated on four hyperspectral data sets. Significant improvements in classification accuracy are achieved by the proposed method with respect to the state-of-the-art methods. Numéro de notice : A2017-473 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2650938 En ligne : https://doi.org/10.1109/TGRS.2017.2650938 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86398
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 6 (June 2017) . - pp 3071 - 3083[article]An internal crown geometric model for conifer species classification with high-density LiDAR data / Aravind Harikumar in IEEE Transactions on geoscience and remote sensing, vol 55 n° 5 (May 2017)
[article]
Titre : An internal crown geometric model for conifer species classification with high-density LiDAR data Type de document : Article/Communication Auteurs : Aravind Harikumar, Auteur ; Francesca Bovolo, Auteur ; Lorenzo Bruzzone, Auteur Année de publication : 2017 Article en page(s) : pp 2924 - 2940 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse en composantes principales
[Termes IGN] classification dirigée
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt
[Termes IGN] houppier
[Termes IGN] modèle de croissance végétale
[Termes IGN] modèle géométrique
[Termes IGN] Pinophyta
[Termes IGN] structure d'un peuplement forestier
[Termes IGN] TrenteRésumé : (Auteur) The knowledge of the tree species is a crucial information that governs the success of precision forest management practice. High-density small footprint multireturn airborne light detection and ranging (LiDAR) scanning can collect a huge amount of point samples containing structural details of the forest vertical profile, which can reveal important structural information of the forest components. LiDAR data have been successfully used to distinguish between coniferous and deciduous/broadleaved tree species. However, species classification within a class (e.g., the conifer class) using LiDAR data is a challenging problem when considering the tree external crown characteristics only. This paper presents a novel method for conifer species classification based on the use of geometric features describing both the internal and external structures of the crown. The internal crown geometric features (IGFs) are defined based on a novel internal branch structure model, which uses 3-D region growing and principal component analysis to delineate the branch structure of a conifer tree accurately. IGFs are used together with external crown geometric features to perform conifer species classification. Three different support vector machines have been considered for classification performance evaluation. The experimental analysis conducted on high-density LiDAR data acquired over a portion of the Trentino region in Italy proves the effectiveness of the proposed method. Numéro de notice : A2017-471 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2656152 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2656152 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86394
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 5 (May 2017) . - pp 2924 - 2940[article]Individual tree basal area increment models for broadleaved forests in Bhutan / Jigme Tenzin in Forestry, an international journal of forest research, vol 90 n° 3 (May 2017)
[article]
Titre : Individual tree basal area increment models for broadleaved forests in Bhutan Type de document : Article/Communication Auteurs : Jigme Tenzin, Auteur ; Karma Tenzin, Auteur ; Hubert Hasenauer, Auteur Année de publication : 2017 Article en page(s) : pp 367 - 380 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation
[Termes IGN] analyse en composantes principales
[Termes IGN] Bhoutan
[Termes IGN] feuillu
[Termes IGN] gestion forestière durable
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de croissance végétale
[Termes IGN] surface terrièreRésumé : (Auteur) The correct and accurate assessments of growing stock (stem volume) in combination with forest growth predictions from models are essential for sustainable forest management. Currently, no such information exists for the broadleaved forests of Bhutan. This study evaluates the important factors of individual tree growth for broadleaved species in Dagana, Bhutan. Data were collected from 96 inventory plots covering forest stand information, tree and stand parameters along with 5-year tree growth increment information from tree cores. Due to the large number of tree species (87), four species groups were created using principal component and cluster analysis to simplify the calibration of individual tree basal area increment (BAI) models. The main determinants of tree growth were shown to be tree size variables and competition within a forest stand. Distance dependent competition indices showed higher correlation to growth than distance independent competition indices. The resulting increment models provided consistent and unbiased estimates of individual tree BAI predictions. Increasing competition levels reduce the productivity of the individual trees. This emphasises the need for crown release to obtain higher individual tree growth. We demonstrate that the BAI models developed in this study can be used to predict tree growth by species group according to different stand density conditions and, if they are verified on a wider scale, could form the basis of sustainable forest management in Bhutan. Numéro de notice : A2017-878 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpw065 En ligne : https://doi.org/10.1093/forestry/cpw065 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91171
in Forestry, an international journal of forest research > vol 90 n° 3 (May 2017) . - pp 367 - 380[article]Evaluation of pan-sharpening methods for spatial and spectral quality / Jagalingam Pushparaj in Applied geomatics, vol 9 n° 1 (March 2017)
[article]
Titre : Evaluation of pan-sharpening methods for spatial and spectral quality Type de document : Article/Communication Auteurs : Jagalingam Pushparaj, Auteur ; Arkal Vittal Hegde, Auteur Année de publication : 2017 Article en page(s) : pp 1 - 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] algorithme de Gram-Schmidt
[Termes IGN] analyse en composantes principales
[Termes IGN] classification Spectral angle mapper
[Termes IGN] évaluation
[Termes IGN] filtre passe-haut
[Termes IGN] image multibande
[Termes IGN] image panchromatique
[Termes IGN] image Quickbird
[Termes IGN] ondelette
[Termes IGN] pansharpening (fusion d'images)
[Termes IGN] qualité géométrique (image)
[Termes IGN] qualité radiométrique (image)
[Termes IGN] transformation de Brovey
[Termes IGN] transformation intensité-teinte-saturationRésumé : (auteur) Many pan-sharpening methods have been proposed to fuse the high spectral and low spatial resolution of multispectral (MS) image with the high spatial resolution of panchromatic (PAN) image to produce a multispectral image with improved spatial resolution. In this study, the effectiveness of pan-sharpening methods such as principal component analysis (PCA), brovey transform (BT), modified intensity hue saturation (M-IHS), multiplicative, wavelet-intensity-hue-saturation (W-IHS), wavelet principal component analysis (W-PCA), hyperspectral colour space (HCS), high-pass filter (HPF), gram-schmidt (GS), subtractive resolution merge (SRM), Fuze Go and Ehlers was assessed and compared by fusing the PAN and MS imagery of Quickbird-2. The qualities of the pan-sharpening methods were evaluated by both visual and quantitative analyses with respect to spatial and spectral fidelity. In quantitative analysis, the spectral indices such as spectral angle mapper (SAM), relative dimensionless global error in synthesis (ERGAS), structural similarity index method (SSIM), relative average spectral error (RASE), correlation coefficient (CC) and universal image quality index (Q) were used. The spatial indices such as spatial correlation coefficient (SCC), gradient and image entropy (E) were used. The result of both analyses revealed that the Ehlers and Fuze Go methods performed better than the other methods. The Ehlers method was superior by retaining the colour information, and Fuze Go best enhanced the spatial details in the fused image. Numéro de notice : A2017-357 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-016-0179-2 Date de publication en ligne : 13/12/2016 En ligne : http://doi.org/10.1007/s12518-016-0179-2 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85763
in Applied geomatics > vol 9 n° 1 (March 2017) . - pp 1 - 12[article]Constrained clustering by constraint programming / Thi-Bich-Hanh Dao in Artificial intelligence, vol 244 (March 2017)
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Titre : Constrained clustering by constraint programming Type de document : Article/Communication Auteurs : Thi-Bich-Hanh Dao, Auteur ; Khanh-Chuong Duong, Auteur ; Christel Vrain, Auteur Année de publication : 2017 Article en page(s) : pp 70 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] analyse de groupement
[Termes IGN] filtrage d'information
[Termes IGN] modélisation
[Termes IGN] optimisation (mathématiques)
[Termes IGN] programmation par contraintesRésumé : (auteur) Constrained Clustering allows to make the clustering task more accurate by integrating user constraints, which can be instance-level or cluster-level constraints. Few works consider the integration of different kinds of constraints, they are usually based on declarative frameworks and they are often exact methods, which either enumerate all the solutions satisfying the user constraints, or find a global optimum when an optimization criterion is specified. In a previous work, we have proposed a model for Constrained Clustering based on a Constraint Programming framework. It is declarative, allowing a user to integrate user constraints and to choose an optimization criterion among several ones. In this article we present a new and substantially improved model for Constrained Clustering, still based on a Constraint Programming framework. It differs from our earlier model in the way partitions are represented by means of variables and constraints. It is also more flexible since the number of clusters does not need to be set beforehand; only a lower and an upper bound on the number of clusters have to be provided. In order to make the model-based approach more efficient, we propose new global optimization constraints with dedicated filtering algorithms. We show that such a framework can easily be embedded in a more general process and we illustrate this on the problem of finding the optimal Pareto front of a bi-criterion constrained clustering task. We compare our approach with existing exact approaches, based either on a branch-and-bound approach or on graph coloring on twelve datasets. Experiments show that the model outperforms exact approaches in most cases. Numéro de notice : A2017-566 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.artint.2015.05.006 En ligne : https://doi.org/10.1016/j.artint.2015.05.006 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86680
in Artificial intelligence > vol 244 (March 2017) . - pp 70 - 94[article]Satellite-based probabilistic assessment of soil moisture using C-band quad-polarized RISAT1 data / Manali Pal in IEEE Transactions on geoscience and remote sensing, vol 55 n° 3 (March 2017)PermalinkTesting spatial heterogeneity in geographically weighted principal components analysis / Javier Roca-Pardiñas in International journal of geographical information science IJGIS, vol 31 n° 3-4 (March-April 2017)PermalinkHyperspectral image classification with canonical correlation forests / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkTélédétection pour l'observation des surfaces continentales, ch. 6. Méthodes de traitement de données lidar / Clément Mallet (2017)PermalinkClass-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkImage processing and GIS techniques applied to high resolution satellite data for lineament mapping of thermal power plant site in Allahabad district, U.P., India / Aniruddha Uniyal in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)PermalinkThe D-FCM partitioned D-BSP tree for massive point cloud data access and rendering / Yi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)PermalinkRegression wavelet analysis for lossless coding of remote-sensing data / Naoufal Amrani in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkAirborne lidar estimation of aboveground forest biomass in the absence of field inventory / António Ferraz in Remote sensing, vol 8 n° 8 (August 2016)PermalinkAutomatic extraction of road networks from GPS traces / Jia Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 8 (August 2016)Permalink