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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]Unsupervised feature learning for land-use scene recognition / Jiayuan Fan in IEEE Transactions on geoscience and remote sensing, vol 55 n° 4 (April 2017)
[article]
Titre : Unsupervised feature learning for land-use scene recognition Type de document : Article/Communication Auteurs : Jiayuan Fan, Auteur ; Tao Chen, Auteur ; Shijian Lu, Auteur Année de publication : 2017 Article en page(s) : pp 2250 - 2261 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] algorithme d'apprentissage
[Termes IGN] analyse discriminante
[Termes IGN] codage
[Termes IGN] image proche infrarouge
[Termes IGN] image RVB
[Termes IGN] invariant
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] reconnaissance automatique
[Termes IGN] Singapour
[Termes IGN] utilisation du solRésumé : (Auteur) This paper proposes a novel unsupervised feature learning algorithm for land-use scene recognition on very high resolution remote sensing imagery. The proposed technique utilizes a multipath sparse coding architecture in order to capture multiple aspects of discriminative structures within complex remote sensing sceneries. Unlike the previous sparse coding and bag-of-visual-words-based techniques that rely on the handcrafted feature descriptors such as scale-invariant feature transform, the proposed technique extracts dense low-level features from the raw data, including the visual (RGB) data and near-infrared (NIR) data, using image patches of varying sizes at different layers. The proposed technique has been evaluated on three data sets, including the 21-category UC Merced landuse RGB data set with a 1-ft spatial resolution, the 9-category ground scene RGB-NIR data set, and the 10-category Singapore land-use RGB-NIR data set with a 0.5-m spatial resolution. The experimental results show that the proposed technique outperforms the state-of-the-art methods. Numéro de notice : A2107-174 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2640186 En ligne : https://doi.org/10.1109/TGRS.2016.2640186 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84723
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 4 (April 2017) . - pp 2250 - 2261[article]Constrained clustering by constraint programming / Thi-Bich-Hanh Dao in Artificial intelligence, vol 244 (March 2017)
[article]
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)
[article]
Titre : Satellite-based probabilistic assessment of soil moisture using C-band quad-polarized RISAT1 data Type de document : Article/Communication Auteurs : Manali Pal, Auteur ; Rajib Maity, Auteur ; Mayank Suman, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 1351 - 1362 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] analyse en composantes principales
[Termes IGN] angle d'incidence
[Termes IGN] bande C
[Termes IGN] coefficient de rétrodiffusion
[Termes IGN] humidité du sol
[Termes IGN] image radar moirée
[Termes IGN] image Risat-1
[Termes IGN] modèle d'incertitude
[Termes IGN] polarimétrie radar
[Termes IGN] polarisation
[Termes IGN] teneur en eau liquideRésumé : (Auteur) This paper attempts to probabilistically estimate the surface soil moisture content (SMC) by using the synthetic aperture radar data provided by radar imaging satellite1. The novelty of this paper lies in: 1) developing a combined index to understand the role of all the backscattering coefficients with different polarization and soil texture information in influencing the SMC; 2) using normalized incidence angles, which enables the model to be applicable for different incidence angles; and 3) determination of uncertainty range of the estimated SMC. The dimensionality problem, which is frequently observed in the multivariate analysis, is reduced in the development of the combined index by the use of supervised principal component analysis (SPCA). The SPCA also ensures the maximum attainable association between the developed combined index and surface SMC above wilting point (WP). The association between the combined index and the surface SMC above WP is modeled through joint probability distribution by using the Frank copula. The model is developed and validated with the field soil moisture values over 334 monitoring points within the study area. The outcomes obtained by applying the proposed model indicate an encouraging potential of the model to be applied for bareland and vegetated land ( Numéro de notice : A2017-153 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2623378 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2623378 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84686
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 3 (March 2017) . - pp 1351 - 1362[article]Testing 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)
[article]
Titre : Testing spatial heterogeneity in geographically weighted principal components analysis Type de document : Article/Communication Auteurs : Javier Roca-Pardiñas, Auteur ; Celestino Ordóñez, Auteur ; Tomás R. Cotos-Yáñez, Auteur ; Rubén Pérez-Álvarez, Auteur Année de publication : 2017 Article en page(s) : pp 676 - 693 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse en composantes principales
[Termes IGN] données localisées
[Termes IGN] hétérogénéité spatiale
[Termes IGN] interpolation par pondération de zones
[Termes IGN] vecteur propreRésumé : (Auteur) We propose a method to evaluate the existence of spatial variability in the covariance structure in a geographically weighted principal components analysis (GWPCA). The method, that is extensive to locally weighted principal components analysis, is based on performing a statistical hypothesis test using the eigenvectors of the PCA scores covariance matrix. The application of the method to simulated data shows that it has a greater statistical power than the current statistical test that uses the eigenvalues of the raw data covariance matrix. Finally, the method was applied to a real problem whose objective is to find spatial distribution patterns in a set of soil pollutants. The results show the utility of GWPCA versus PCA. Numéro de notice : A2017-079 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1224886 En ligne : http://dx.doi.org/10.1080/13658816.2016.1224886 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84342
in International journal of geographical information science IJGIS > vol 31 n° 3-4 (March-April 2017) . - pp 676 - 693[article]Réservation
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